Resourse transporation systems and methods

ABSTRACT

In some embodiments, a system for resource transportation comprises a routing command subsystem. In some embodiments, the routing command subsystem is configured to be communicably coupled to a first input device at a first location, the first input device configured to determine a first resource factor of a resource at the first location and a location input device associated with a transporter, the transporter configured to transport the resource, the location input device configured to determine a transporter location. In some embodiments, the routing command subsystem is further configured to change a first endpoint of a transporter route to a first alternate location based at least in part on the first resource factor and the transporter location.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority to U.S. Provisional Application No.62/659,109 filed on Apr. 17, 2018 entitled “Logistics System” and U.S.Provisional Application No. 62/789,294 filed on Jan. 7, 2019 entitled“Resource Transportation Systems and Method,” each of which isincorporated herein by reference.

TECHNICAL FIELD

Embodiments relate to resource transportation systems.

BACKGROUND

For various applications, such as hydraulic fracturing, it is desirableto transport sand and other materials from sources such as mines todestinations such as wells. In conventional systems, a fixed number oftransports deliver the materials from sources to destinations accordingto fixed routes and schedules. This can result in inefficiencies andunderutilization of transportation resources. In some embodiments, thesystems and methods described herein overcome these and other problems.

SUMMARY

In some embodiments, a system for resource transportation comprises arouting command subsystem. In some embodiments, the routing commandsubsystem is configured to be communicably coupled to a first inputdevice at a first location, the first input device configured todetermine a first resource factor of a resource at the first locationand a location input device associated with a transporter, thetransporter configured to transport the resource, the location inputdevice configured to determine a transporter location. In someembodiments, the routing command subsystem is further configured tochange a first endpoint of a transporter route to a first alternatelocation based at least in part on the first resource factor and thetransporter location.

In some embodiments, the first location comprises a supply node. In someembodiments, the first location comprises a demand node. In someembodiments, system further comprises a second input device at a secondlocation, the second input device configured to determine a secondresource factor at the second location. In some embodiments, thechanging the first endpoint of the transporter route further compriseschanging the first endpoint of the transporter route to the firstalternate location based at least in part on the second resource factor.

In some embodiments, the first location comprises a source node and thesecond location comprises a destination node. In some embodiments, thefirst input device comprises a sensor. In some embodiments, the firstinput device comprises a data entry device. In some embodiments, thelocation input device comprises a sensor. In some embodiments, thelocation input device comprises a data entry device.

In some embodiments, the first resource factor comprises an amount ofthe resource. In some embodiments, the amount of the resource comprisesa quantity of the resource at the first location. In some embodiments,the amount of the resource comprises a quantity of the resource consumedat the first location. In some embodiments, the first resource factorcomprises a rate of change of the resource. In some embodiments, thefirst resource factor comprises a comparison of a quantity of theresource withdrawn at a supply node with a scheduled demand for theresource at a demand node.

In some embodiments, the first input device determines the firstresource factor based at least in part on a real-time input. In someembodiments, the first input device determines the first resource factorbased at least in part on a periodic input. In some embodiments, thelocation input device determines the transporter location based at leastin part on a real-time input. In some embodiments, the location inputdevice determines the transporter location based at least in part on aperiodic input.

In some embodiments, the system further comprises a second input deviceassociated with the transporter, the second input device configured todetermine a second resource factor of the resource associated with thetransporter. In some embodiments, the second resource factor comprisesan amount of the resource transported by the transporter. In someembodiments, the first endpoint of the transporter route comprises asupply node. In some embodiments, the first endpoint the transporterroute comprises a demand node.

In some embodiments, the routing command subsystem is further configuredto determine a transporter resource factor, wherein the transporterresource factor comprises a number of transporters transporting theresource. In some embodiments, the routing command subsystem is furtherconfigured to direct a second transporter to transport the resourcebased at least in part on the first resource factor and the transporterlocation. In some embodiments, the routing command subsystem is furtherconfigured to change a schedule for the transporter to transport theresource based at least in part on the first resource factor and thetransporter location.

In some embodiments, the routing command subsystem is further configuredto change a route of the transporter to include a first intermediatelocation based at least in part on the first resource factor and thetransporter location. In some embodiments, the changing the firstendpoint of the transporter route further comprises predicting anavailability of the resource at a supply node. In some embodiments, thechanging the first endpoint of the transporter route further comprisespredicting a demand of the resource at a demand node. In someembodiments, the changing the first endpoint of the transporter routefurther comprises predicting a number of transporters available totransport the resource.

In some embodiments, the first resource factor depends on a secondresource factor associated with a second resource. In some embodiments,the resource comprises at least one sand, chemicals, water, oil, gas,equipment, or personnel. In some embodiments, the transporter comprisesat least one of a rail car, a truck, or pipeline. In some embodiments,the first input device comprises at least one of a node site sensor, afracking van sensor, or a supply node sensor. In some embodiments, therouting command subsystem is further configured to change the firstendpoint of the transporter route to the first alternate location basedat least in part on the first resource factor, the transporter location,and a driver factor. In some embodiments, the driver factor comprises atleast one of a current duty status or a number of hours of serviceremaining.

In some embodiments, a system for resource transportation comprises adriver selection subsystem configured to be communicably coupled to afirst input device at a first location, the first input deviceconfigured to determine a first resource factor of a resource at thefirst location; a location input device associated with a transporter,the location input device configured to determine a transporterlocation; a first driver input device associated with a first driver,the first driver input device configured to determine a first driverfactor; and a second driver input device associated with a seconddriver, the second driver input device configured to determine a seconddriver factor. In some embodiments, the driver selection subsystem isfurther configured to associate at least one of the first driver or thesecond driver with the transporter based at least in part on the firstresource factor, the transporter location, the first driver factor, andthe second driver factor.

In some embodiments, the first location comprises a supply node. In someembodiments, the first location comprises a demand node. In someembodiments, the first input device comprises a sensor. In someembodiments, the first input device comprises a data entry device. Insome embodiments, the location input device comprises a sensor. In someembodiments, the location input device comprises a data entry device. Insome embodiments, the first driver input device comprises a sensor. Insome embodiments, the first driver input device comprises a data entrydevice. In some embodiments, the second driver input device comprises asensor. In some embodiments, the second driver input device comprises adata entry device.

In some embodiments, the first resource factor comprises an amount ofthe resource. In some embodiments, the amount of the resource comprisesa quantity of the resource at the first location. In some embodiments,the amount of the resource comprises a quantity of the resource consumedat the first location. In some embodiments, the first resource factorcomprises a rate of change of the resource. In some embodiments, thefirst resource factor comprises a comparison of a quantity of theresource withdrawn at a supply node with a scheduled demand for theresource at a demand node.

In some embodiments, the first input device determines the firstresource factor based at least in part on a real-time input. In someembodiments, the first input device determines the first resource factorbased at least in part on a periodic input. In some embodiments, thelocation input device determines the transporter location based at leastin part on a real-time input. In some embodiments, the location inputdevice determines the transporter location based at least in part on aperiodic input.

In some embodiments, the system further comprises a second input deviceassociated with the transporter, the second input device configured todetermine a second resource factor of the resource associated with thetransporter. In some embodiments, the system further comprises thesecond resource factor comprises an amount of the resource transportedby the transporter.

In some embodiments, the driver selection subsystem is furtherconfigured to determine a transporter resource factor, wherein thetransporter resource factor comprises a number of transporterstransporting the resource. In some embodiments, the driver selectionsubsystem is further configured to direct a second transporter totransport the resource based at least in part on the first resourcefactor and the transporter location. In some embodiments, the driverselection subsystem is further configured to change a schedule for thetransporter to transport the resource based at least in part on thefirst resource factor and the transporter location. In some embodiments,the driver selection subsystem is further configured to change a routeof the transporter to include a first intermediate location based atleast in part on the first resource factor and the transporter location.

In some embodiments, the first resource factor depends on a secondresource factor associated with a second resource. In some embodiments,the resource comprises at least one sand, chemicals, water, oil, gas,equipment, or personnel. In some embodiments, the transporter comprisesat least one of a rail car, a truck, or pipeline. In some embodiments,the first input device comprises at least one of a node site sensor, afracking van sensor, or a supply node sensor. In some embodiments, thefirst driver factor comprises at least one of a first current dutystatus or a first number of hours of service remaining. In someembodiments, the second driver factor comprises at least one of a firstcurrent duty status or a first number of hours of service remaining.

In some embodiments, a system for resource transportation comprises atransporter selection subsystem configured to be communicably coupledto: a first input device at a first location, the first input deviceconfigured to determine a first resource factor of a resource at thefirst location; a first location input device associated with a firsttransporter, the first location input device configured to determine afirst transporter location; a second location input device associatedwith a second transporter, the first location input device configured todetermine a second transporter location; a driver input deviceassociated with a driver, the driver input device configured todetermine a driver factor. In some embodiments, the transporterselection subsystem is further configured to select at least one of thefirst transporter or the second transporter to transport the resourcebased at least in part on the first resource factor, the firsttransporter location, the second transporter location, and the driverfactor.

In some embodiments, the first location comprises a supply node. In someembodiments, the first location comprises a demand node. In someembodiments, the first input device comprises a sensor. In someembodiments, the first input device comprises a data entry device. Insome embodiments, the first location input device comprises a sensor. Insome embodiments, the first location input device comprises a data entrydevice. In some embodiments, the second location input device comprisesa sensor. In some embodiments, the second location input devicecomprises a data entry device.

In some embodiments, the first resource factor comprises an amount ofthe resource. In some embodiments, the amount of the resource comprisesa quantity of the resource at the first location. In some embodiments,the amount of the resource comprises a quantity of the resource consumedat the first location. In some embodiments, the first resource factorcomprises a rate of change of the resource. In some embodiments, thefirst resource factor comprises a comparison of a quantity of theresource withdrawn at a supply node with a scheduled demand for theresource at a demand node.

In some embodiments, the first input device determines the firstresource factor based at least in part on a real-time input. In someembodiments, the first input device determines the first resource factorbased at least in part on a periodic input. In some embodiments, thefirst location input device determines the transporter location based atleast in part on a real-time input. In some embodiments, the firstlocation input device determines the transporter location based at leastin part on a periodic input. In some embodiments, the second locationinput device determines the transporter location based at least in parton a real-time input.

In some embodiments, the second location input device determines thetransporter location based at least in part on a periodic input. In someembodiments, the system further comprises a second input deviceassociated with the transporter, the second input device configured todetermine a second resource factor of the resource associated with thetransporter. In some embodiments, the second resource factor comprisesan amount of the resource transported by the transporter. In someembodiments, the transporter selection subsystem is further configuredto determine a transporter resource factor, wherein the transporterresource factor comprises a number of transporters transporting theresource.

In some embodiments, the transporter selection subsystem is furtherconfigured to direct a second transporter to transport the resourcebased at least in part on the first resource factor and the firsttransporter location. In some embodiments, the transporter selectionsubsystem is further configured to change a schedule for the transporterto transport the resource based at least in part on the first resourcefactor and the first transporter location. In some embodiments, therouting command subsystem is further configured to change a route of thetransporter to include a first intermediate location based at least inpart on the first resource factor and the first transporter location.

In some embodiments, the first resource factor depends on a secondresource factor associated with a second resource. In some embodiments,the resource comprises at least one sand, chemicals, water, oil, gas,equipment, or personnel. In some embodiments, the first transportercomprises at least one of a rail car, a truck, or pipeline. In someembodiments, the first input device comprises at least one of a nodesite sensor, a fracking van sensor, or a supply node sensor. In someembodiments, the driver factor comprises at least one of a current dutystatus or a number of hours of service remaining.

In some embodiments, a system for resource transportation comprises apredictive utilization subsystem configured to be communicably coupledto: a first input device at a first location, the first input deviceconfigured to determine a first resource factor of a resource at thefirst location, wherein the first location comprises a demand node; asecond input device, the second input device configured to determine asecond factor; the predictive utilization subsystem is furtherconfigured to predict availability based at least in part on the firstresource factor and the second factor.

In some embodiments, the second factor comprises a supply of theresource at a supply node. In some embodiments, the second factorcomprises a supply of the resource at an intermediary node. In someembodiments, the second factor comprises a number of transporterstransporting the resource. In some embodiments, availability comprises apredicted availability of the resource at a supply node. In someembodiments, availability comprises a predicted availability of theresource at an intermediary node. In some embodiments, the availabilitycomprises a predicted availability of transporters to transport theresource. In some embodiments, availability comprises a predictedavailability of the resource at the demand node. In some embodiments,availability comprises a predicted demand of the resource at the demandnode.

In some embodiments, the first input device comprises a sensor. In someembodiments, the first input device comprises a data entry device. Insome embodiments, the second input device comprises a sensor. In someembodiments, the second input device comprises a data entry device. Insome embodiments, the first resource factor comprises an amount of theresource. In some embodiments, the first resource factor comprises arate of change of the resource. In some embodiments, the first resourcefactor comprises a comparison of a quantity of the resource withdrawn ata supply node with a scheduled demand for the resource at a demand node.

In some embodiments, the first input device determines the firstresource factor based at least in part on a real-time input. In someembodiments, the first input device determines the first resource factorbased at least in part on a periodic input. In some embodiments, thesecond input device determines the second resource factor based at leastin part on a real-time input. In some embodiments, the second inputdevice determines the second resource factor based at least in part on aperiodic input.

In some embodiments, the system further comprises a third input deviceassociated with the transporter, the third input device configured todetermine a third resource factor of the resource associated with thetransporter. In some embodiments, the third resource factor comprises anamount of the resource transported by the transporter. In someembodiments, the first resource factor depends on a third resourcefactor associated with a second resource. In some embodiments, thesecond resource factor depends on a third resource factor associatedwith a second resource.

In some embodiments, a method for resource transportation comprisesdetermining a first resource factor of a resource at a first location;determining a transporter location of a transporter configured totransport the resource; and changing a first endpoint of a transporterroute to a first alternate location based at least in part on the firstresource factor and the transporter location.

In some embodiments, the first location comprises a supply node. In someembodiments, the first location comprises a demand node. In someembodiments, the method further comprises determining a second resourcefactor at the second location; wherein the changing the first endpointof the transporter route further comprise changing the first endpoint ofthe transporter route to the first alternate location based at least inpart on the second resource factor. In some embodiments, the firstlocation comprises a source node and the second location comprises adestination node.

In some embodiments, determining the first resource factor of theresource at the first location comprises determining the first resourcefactor at least in part using a first input device. In some embodiments,the first input device comprises a sensor. In some embodiments, thefirst input device comprises a data entry device. In some embodiments,determining the transporter location of the transporter configured totransport the resource comprises determining the transporter locationusing a first location input device.

In some embodiments, the location input device comprises a sensor. Insome embodiments, the location input device comprises a data entrydevice. In some embodiments, the first resource factor comprises anamount of the resource. In some embodiments, the amount of the resourcecomprises a quantity of the resource at the first location. In someembodiments, the amount of the resource comprises a quantity of theresource consumed at the first location. In some embodiments, the firstresource factor comprises a rate of change of the resource. In someembodiments, the first resource factor comprises a comparison of aquantity of the resource withdrawn at a supply node with a scheduleddemand for the resource at a demand node.

In some embodiments, determining the first resource factor comprisesdetermining the first resource factor based at least in part on areal-time input. In some embodiments, determining the first resourcefactor comprises determining the first resource factor based at least inpart on a periodic input. In some embodiments, determining thetransporter location comprises determining the transporter locationbased at least in part on a real-time input. In some embodiments,determining the transporter location comprises determining thetransporter location based at least in part on a periodic input.

In some embodiments, the method further comprises determining a secondresource factor of the resource associated with the transporter. In someembodiments, the second resource factor comprises an amount of theresource transported by the transporter. In some embodiments, the firstendpoint of the transporter route comprises a supply node. In someembodiments, the first endpoint the transporter route comprises a demandnode.

In some embodiments, the method further comprises determining atransporter resource factor, wherein the transporter resource factorcomprises a number of transporters transporting the resource. In someembodiments, the method further comprises directing a second transporterto transport the resource based at least in part on the first resourcefactor and the transporter location.

In some embodiments, the method further comprises changing a schedulefor the transporter to transport the resource based at least in part onthe first resource factor and the transporter location. In someembodiments, the method further comprises changing a route of thetransporter to include a first intermediate location based at least inpart on the first resource factor and the transporter location. In someembodiments, the changing the first endpoint of the transporter routefurther comprises predicting an availability of the resource at a supplynode. In some embodiments, the changing the first endpoint of thetransporter route further comprises predicting a demand of the resourceat a demand node. In some embodiments, the changing the first endpointof the transporter route further comprises predicting a number oftransporters available to transport the resource.

In some embodiments, the first resource factor depends on a secondresource factor associated with a second resource. In some embodiments,the resource comprises at least one sand, chemicals, water, oil, gas,equipment, or personnel. In some embodiments, the transporter comprisesat least one of a rail car, a truck, or pipeline.

In some embodiments, determining the first resource factor of theresource at the first location comprises determining the first resourcefactor at least in part using a first input device and wherein the firstinput device comprises at least one of a node site sensor, a frackingvan sensor, or a supply node sensor. In some embodiments, the methodfurther comprises changing the first endpoint of the transporter routeto the first alternate location based at least in part on the firstresource factor, the transporter location, and a driver factor. In someembodiments, the driver factor comprises at least one of a current dutystatus or a number of hours of service remaining.

In some embodiments, a method for resource transportation comprisesdetermining a first resource factor of a resource at a first location;determining a transporter location of a transporter; determining a firstdriver factor for a first driver; determining a second driver factor fora second driver; and associating at least one of the first driver or thesecond driver with the transporter based at least in part on the firstresource factor, the transporter location, the first driver factor, andthe second driver factor.

In some embodiments, the first location comprises a supply node. In someembodiments, the first location comprises a demand node. In someembodiments, determining the first resource factor comprises determiningthe first resource factor at least in part using a first input device.In some embodiments, the first input device comprises a sensor. In someembodiments, the first input device comprises a data entry device. Insome embodiments, determining the transporter location comprisesdetermining the transporter location at least in part using a locationinput device. In some embodiments, the location input device comprises asensor. In some embodiments, the location input device comprises a dataentry device.

In some embodiments, determining the first driver factor comprisesdetermining the first driver factor at least in part using a firstdriver input device. In some embodiments, the first driver input devicecomprises a sensor. In some embodiments, the first driver input devicecomprises a data entry device. In some embodiments, determining thesecond driver factor comprises determining the second driver factor atleast in part using a second driver input device. In some embodiments,the second driver input device comprises a sensor. In some embodiments,the second driver input device comprises a data entry device.

In some embodiments, the first resource factor comprises an amount ofthe resource. In some embodiments, the amount of the resource comprisesa quantity of the resource at the first location. In some embodiments,the amount of the resource comprises a quantity of the resource consumedat the first location. In some embodiments, the first resource factorcomprises a rate of change of the resource. In some embodiments, thefirst resource factor comprises a comparison of a quantity of theresource withdrawn at a supply node with a scheduled demand for theresource at a demand node.

In some embodiments, the method further comprises determining the firstresource factor based at least in part on a real-time input. In someembodiments, the method further comprises determining the first resourcefactor based at least in part on a periodic input. In some embodiments,the method further comprises determining the transporter location basedat least in part on a real-time input. In some embodiments, the methodfurther comprises determining the transporter location based at least inpart on a periodic input. In some embodiments, the method furthercomprises determining a second resource factor of the resourceassociated with the transporter.

In some embodiments, the second resource factor comprises an amount ofthe resource transported by the transporter. In some embodiments, themethod further comprises determining a transporter resource factor,wherein the transporter resource factor comprises a number oftransporters transporting the resource. In some embodiments, the methodfurther comprises directing a second transporter to transport theresource based at least in part on the first resource factor and thetransporter location.

In some embodiments, the method further comprises changing a schedulefor the transporter to transport the resource based at least in part onthe first resource factor and the transporter location. In someembodiments, the method further comprises changing a route of thetransporter to include a first intermediate location based at least inpart on the first resource factor and the transporter location. In someembodiments, the first resource factor depends on a second resourcefactor associated with a second resource.

In some embodiments, the resource comprises at least one sand,chemicals, water, oil, gas, equipment, or personnel. In someembodiments, the transporter comprises at least one of a rail car, atruck, or pipeline. In some embodiments, the first driver factorcomprises at least one of a first current duty status or a first numberof hours of service remaining. In some embodiments, the second driverfactor comprises at least one of a first current duty status or a firstnumber of hours of service remaining.

In some embodiments, method for resource transportation comprisesdetermining a first resource factor of a resource at a first location;determining a first transporter location for a first transporter;determining a second transporter location for a second transporter;determining a driver factor for a driver; and selecting at least one ofthe first transporter or the second transporter to transport theresource based at least in part on the first resource factor, the firsttransporter location, the second transporter location, and the driverfactor.

In some embodiments, the first location comprises a supply node. In someembodiments, the first location comprises a demand node. In someembodiments, determining the first resource factor comprises determiningthe first resource factor at least in part using a first input device.In some embodiments, the first input device comprises a sensor. In someembodiments, the first input device comprises a data entry device. Insome embodiments, determining the first transporter location comprisesdetermining the transporter location at least in part using a firstlocation input device.

In some embodiments, the first location input device comprises a sensor.In some embodiments, the first location input device comprises a dataentry device. In some embodiments, determining the second transporterlocation comprises determining the transporter location at least in partusing a second location input device. In some embodiments, the secondlocation input device comprises a sensor. In some embodiments, thesecond location input device comprises a data entry device. In someembodiments, the first resource factor comprises an amount of theresource. In some embodiments, the amount of the resource comprises aquantity of the resource at the first location. In some embodiments, theamount of the resource comprises a quantity of the resource consumed atthe first location.

In some embodiments, the first resource factor comprises a rate ofchange of the resource. In some embodiments, the first resource factorcomprises a comparison of a quantity of the resource withdrawn at asupply node with a scheduled demand for the resource at a demand node.In some embodiments, the method further comprises determining the firstresource factor based at least in part on a real-time input. In someembodiments, the method further comprises the first resource factorbased at least in part on a periodic input. In some embodiments, themethod further comprises determining the transporter location based atleast in part on a real-time input. In some embodiments, the methodfurther comprises determining the transporter location based at least inpart on a periodic input. In some embodiments, the method furthercomprises determining the transporter location based at least in part ona real-time input. In some embodiments, the method further comprisesdetermining the transporter location based at least in part on aperiodic input. In some embodiments, the method further comprisesdetermining a second resource factor of the resource associated with thetransporter.

In some embodiments, the second resource factor comprises an amount ofthe resource transported by the transporter. In some embodiments, themethod further comprises determining a transporter resource factor,wherein the transporter resource factor comprises a number oftransporters transporting the resource. In some embodiments, the methodfurther comprises directing a second transporter to transport theresource based at least in part on the first resource factor and thefirst transporter location. In some embodiments, the method furthercomprises changing a schedule for the transporter to transport theresource based at least in part on the first resource factor and thefirst transporter location.

In some embodiments, the method further comprises changing a route ofthe transporter to include a first intermediate location based at leastin part on the first resource factor and the first transporter location.In some embodiments, the first resource factor depends on a secondresource factor associated with a second resource. In some embodiments,the resource comprises at least one sand, chemicals, water, oil, gas,equipment, or personnel. In some embodiments, the first transportercomprises at least one of a rail car, a truck, or pipeline. In someembodiments, the driver factor comprises at least one of a current dutystatus or a number of hours of service remaining.

In some embodiments, a method for resource transportation comprisesdetermining a first resource factor of a resource at a first location,wherein the first location comprises a demand node; determining a secondfactor; and predicting availability based at least in part on the firstresource factor and the second factor.

In some embodiments, the second factor comprises a supply of theresource at a supply node. In some embodiments, the second factorcomprises a supply of the resource at an intermediary node. In someembodiments, the second factor comprises a number of transporterstransporting the resource. In some embodiments, availability comprises apredicted availability of the resource at a supply node. In someembodiments, availability comprises a predicted availability of theresource at an intermediary node. In some embodiments, availabilitycomprises a predicted availability of transporters to transport theresource. In some embodiments, availability comprises a predictedavailability of the resource at the demand node. In some embodiments,availability comprises a predicted demand of the resource at the demandnode.

In some embodiments, determining the first resource factor comprisesdetermining the first resource factor at least in part using a firstinput device. In some embodiments, first input device comprises asensor. In some embodiments, the first input device comprises a dataentry device. In some embodiments, determining the second resourcefactor comprises determining the second resource factor at least in partusing a second input device. In some embodiments, the second inputdevice comprises a sensor. In some embodiments, the second input devicecomprises a data entry device. In some embodiments, the first resourcefactor comprises an amount of the resource. In some embodiments, thefirst resource factor comprises a rate of change of the resource. Insome embodiments, the first resource factor comprises a comparison of aquantity of the resource withdrawn at a supply node with a scheduleddemand for the resource at a demand node.

In some embodiments, the method further comprises determining the firstresource factor based at least in part on a real-time input. In someembodiments, the method further comprises determining the first resourcefactor based at least in part on a periodic input. In some embodiments,the method further comprises determining the second resource factorbased at least in part on a real-time input. In some embodiments, themethod further comprises determining the second resource factor based atleast in part on a periodic input. In some embodiments, the methodfurther comprises determining a third resource factor of the resourceassociated with the transporter. In some embodiments, the third resourcefactor comprises an amount of the resource transported by thetransporter. In some embodiments, the first resource factor depends on athird resource factor associated with a second resource. In someembodiments, the second resource factor depends on a third resourcefactor associated with a second resource.

In some embodiments, a system for resource transportation comprises: aproduction operation at a first location, the production operation usinga resource at a use rate that varies over time, wherein the use ratefurther depends on availability of other resources at the firstlocation; an amount sensor at the production operation, the amountsensor configured to determine an amount of the resource at theproduction operation; a source repository of the resource at a secondlocation remote from the first location; a transporter configured totransport the resource; a location sensor associated with thetransporter, the location sensor configured to determine a transporterlocation; and a control system communicably coupled to the locationsensor and the amount sensor. In some embodiments, the control system isfurther configured to receive information about the use rate, and toreroute the transporter to one of the first location and the secondlocation based at least in part on the amount, the use rate, and thetransporter location.

In some embodiments, a system for resource transportation comprises arouting command subsystem configured to be communicably coupled to: anamount sensor at a production operation at a first location, the amountsensor configured to determine an amount of a resource at the productionoperation, the production operation using the resource at a use ratethat varies over time, wherein the use rate further depends onavailability of other resources at the first location; a location sensorassociated with a transporter, the transporter configured to transportthe resource, the location sensor configured to determine a transporterlocation. In some embodiments, the routing command subsystem is furtherconfigured to receive information about the use rate; and reroute thetransporter based at least in part on the amount and the transporterlocation.

In some embodiments, the resource comprises sand. In some embodiments,the transporter comprises a rail car. In some embodiments, thetransporter comprises a truck. In some embodiments, the amount sensorcomprises at least one of a site sensor, a fracking van sensor, a sandmine sensor, or a transload sensor. In some embodiments, rerouting thetransporter comprises routing the transporter via a transload.

In some embodiments, the routing command subsystem is further configuredto reroute the transporter based in part on a quantity of the resourceat the first location. In some embodiments, the routing commandsubsystem is further configured to reroute the transporter based in parton a total quantity of the resource consumed at the first location. Insome embodiments, the routing command subsystem is further configured toreroute the transporter based in part on a predicted consumption rate atthe first location. In some embodiments, the routing command subsystemis further configured to reroute the transporter based in part on apredicted consumption rate at a second site.

In some embodiments, a method for resource transportation comprises:determining an amount of a resource at a production operation at a firstlocation, the resource being used at a use rate that varies over time,wherein the use rate further depends on availability of other resourcesat the first location; transporting the resource with a transporter;determining a transporter location; receiving information about the userate; and rerouting the transporter based at least in part on the amountand the transporter location.

In some embodiments, the resource comprises sand. In some embodiments,the transporter comprises a rail car. In some embodiments, thetransporter comprises a truck. In some embodiments, rerouting thetransporter comprises routing the transporter via a transload. In someembodiments, the method further comprises rerouting the transporterbased in part on a quantity of the resource at the first location. Insome embodiments, the method further comprises rerouting the transporterbased in part on a total quantity of the resource consumed at the firstlocation. In some embodiments, the method further comprises reroutingthe transporter based in part on a predicted consumption rate at thefirst location. In some embodiments, the method further comprisesrerouting the transporter based in part on a predicted consumption rateat a second location.

In some embodiments, a method of automatic reconciliation comprisesreceiving logistics data from a logistics vendor; receiving payload datafrom a payload vendor; and automatically reconciling the logistics datawith the payload data.

In some embodiments, the logistics data comprises a first dateidentifier; the payload data comprises a second date identifier; andperforming the automatic reconciliation comprises matching the firstdate identifier with the second date identifier. In some embodiments,the first date identifier comprises at least one of a first arrival dateor a first departure date and the second date identifier comprises atleast one of a second arrival date or a second departure date. In someembodiments, the logistics data comprises a first purchase order number;the payload data comprises a second purchase order number; andperforming the automatic reconciliation comprises matching the firstpurchase order number with the second purchase order number. In someembodiments, the logistics data comprises a first transporteridentifier; the payload data comprises a second transporter identifier;and performing the automatic reconciliation comprises matching the firsttransporter identifier and the second transporter identifier.

In some embodiments, the method further comprises automaticallygenerating pricing data based on a result of the automaticallyreconciling the logistics data with the payload data. In someembodiments, the payload data comprises a bill of lading number; and theautomatic reconciliation comprises associating the bill of lading numberwith the logistics data. In some embodiments, the payload data comprisesa payload weight; and the automatic reconciliation comprises associatingthe payload weight with the logistics data. In some embodiments,performing the automatic reconciliation comprises matching the logisticsdata and the payload data in a predetermined order. In some embodiments,performing the automatic reconciliation comprises matching the logisticsdata and the payload data using a fuzzy logic algorithm.

In some embodiments, a system for automatic reconciliation comprises atransceiver configured to: receive logistics data from a logisticsvendor, and receive payload data from a payload vendor; and a processorconfigured to automatically reconcile the logistics data and the payloaddata.

In some embodiments, the logistics data comprises a first dateidentifier; the payload data comprises a second date identifier; and theprocessor is configured to automatically reconcile the logistics dataand the payload data by matching the first date identifier with thesecond date identifier. In some embodiments, the first date identifiercomprises at least one of a first arrival date or a first departure dateand the second date identifier comprises at least one of a secondarrival date or a second departure date. In some embodiments, thelogistics data comprises a first purchase order number; the payload datacomprises a second purchase order number; and the processor isconfigured to automatically reconcile the logistics data and the payloaddata by matching the first purchase order number with the secondpurchase order number.

In some embodiments, the logistics data comprises a first transporteridentifier; the payload data comprises a second transporter identifier;and the processor is configured to automatically reconcile the logisticsdata and the payload data by matching the first transporter identifierand the second transporter identifier.

In some embodiments, the processor is further configured toautomatically generate pricing data based on a result of theautomatically reconciling the logistics data with the payload data. Insome embodiments, the payload data comprises a bill of lading number;and the processor is configured to automatically reconcile the logisticsdata and the payload data by associating the bill of lading number withthe logistics data. In some embodiments, the payload data comprises apayload weight; and the processor is configured to automaticallyreconcile the logistics data and the payload data by associating thepayload weight with the logistics data.

In some embodiments, the processor is configured to automaticallyreconcile the logistics data and the payload data by matching thelogistics data and the payload data in a predetermined order. In someembodiments, the processor is configured to automatically reconcile thelogistics data and the payload data by matching the logistics data andthe payload data using a fuzzy logic.

While multiple embodiments are disclosed, still other embodiments of thepresent disclosure will become apparent to those skilled in the art fromthe following detailed description, which shows and describesillustrative embodiments. Accordingly, the drawings and detaileddescription are to be regarded as illustrative in nature and notrestrictive.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a logistics system according to an exemplaryembodiment.

FIG. 2 shows a graph that illustrates the impact of shifting sand demandand supply on on-site storage at a well according to an exemplaryembodiment.

FIG. 3 shows an example of value created for various stakeholders by alogistics system according to an exemplary embodiment.

FIG. 4 illustrates a logistics system according to an exemplaryembodiment.

FIG. 5 illustrates an exemplary application of a logistics system to anillustrative example with two wells and two mines according to anexemplary embodiment.

FIG. 6 illustrates a logistics method according to an exemplaryembodiment.

FIG. 7 shows a screen from a job setup application according to anexemplary embodiment.

FIG. 8 shows an example of an optimized schedule for one mine and onewell according to an exemplary embodiment.

FIG. 9 shows a screenshot from a dispatch module according to anexemplary embodiment.

FIG. 10 shows a screenshot of a driver module according to an exemplaryembodiment.

FIG. 11 shows a screenshot from a logistics module indicating a statusof multiple wells, their associated mines, and the status of NPT anddemurrage for each as related to a pre-defined alerting thresholdaccording to an exemplary embodiment.

FIG. 12 shows a screenshot from a logistics module indicating an actualand predicted status of on-site storage at a well according to anexemplary embodiment.

FIG. 13 shows a screenshot of a logistics module indicating availablemines or transloads along with their status and current wait timesaccording to an exemplary embodiment.

FIG. 14 shows a screenshot of a logistics module's well/mine statusreporting according to an exemplary embodiment.

FIG. 15 shows a screenshot of a logistics module's well/mine statusreporting according to an exemplary embodiment.

FIG. 16 shows a screenshot from a logistics module indicating a mineestimated to be down for 56 hours according to an exemplary embodiment.

FIG. 17 illustrates an example of a logistics module indicating afailure at one or more mines that have deliveries scheduled for thelisted wells and potential NPT and demurrage impacts according to anexemplary embodiment.

FIG. 18 shows an example of a predicted impact on well site storage ofsand from the change illustrated in FIG. 17 according to an exemplaryembodiment.

FIG. 19 shows a table demonstrating an example of how different failurepoints are detected, the failure points themselves, and examplesoptimizations according to an exemplary embodiment.

FIG. 20 shows a screen shot displaying suggested optimizations for a“last mile operator” (LMO) according to an exemplary embodiment.

FIG. 21 shows an example of a logistics system with three distant mineswhich ship to three different transload facilities, two local mines, andeight wells consuming the sand according to an exemplary embodiment.

FIG. 22 shows an example of a logistics system with three distant mineswhich ship to three different transload facilities, two local mines, andeight wells consuming the sand with multiple points of failure accordingto an exemplary embodiment.

FIG. 23 shows a table of optimizations in response to detected points offailure according to an exemplary embodiment.

FIG. 24 shows a conceptual representation of an optimization algorithmaccording to one or more exemplary embodiments.

FIG. 25 illustrates a reconciliation method according to an exemplaryembodiment.

FIG. 26 shows a conceptual representation of an automatic reconciliationaccording to an exemplary embodiment.

FIG. 27 illustrates a logistics system with an exemplary payloadordering module according to an exemplary embodiment.

While embodiments of the disclosure are amenable to variousmodifications and alternative forms, specific embodiments have beenshown by way of example in the drawings and are described in detailbelow. The intention, however, is not to limit the invention to theparticular embodiments described. On the contrary, the invention isintended to cover all modifications, equivalents, and alternativesfalling within the scope of the invention as defined by the appendedclaims.

DETAILED DESCRIPTION

According to some exemplary embodiments, systems and methods disclosedherein may improve logistical operations. According to some exemplaryembodiments, logistics systems and methods such as resourcetransportation systems and methods may, for example, improve logisticsoperations by forecasting payload demand, forecasting supplyavailability, forecasting payload availability, forecasting transporteravailability, among other factors. In some embodiments, supplyavailability may include payload availability and transport (e.g.,transporter) availability. In some embodiments, additional factors may,for example, include trucker availability, driver availability, amongothers. Payloads may include different types of materials (e.g., sand,water, chemicals, other materials, drill pipes, equipment, personnel,etc.). Payloads such as sand may also have different types, e.g.,different types of sand. In some cases, different transports may be usedto transport different types of payloads. For example, different typesof trucks/drivers may be used for different payloads (e.g., sand vschemicals) and for different types of a particular payload (e.g.,different types of sand). Storage equipment (e.g., silos) may alsodepend on the type of payload being stored. Transporters can, forexample, have different characteristics such as operator type, equipmenttype, type of destination it can deliver to, and types of pick-uppoints, types of payload. Different types of trucks (e.g., pneumatic,flatbed, etc.) may be used depending on payload type. Equipment andtruck selection may also depend on type of payload and transitionsbetween payloads (e.g., cleaning may be desirable after transportingsand before transporting other payloads). Another factor which may betracked (e.g., by measuring weight or using one or more other sensors),and which may affect efficiency is the quantity of payload carried byeach transporter, for example, whether the transport capacity of a truckis fully utilized or whether trucks are only partially full. In someembodiments, these and other factors may be tracked in real-time and/orperiodically and real-time and/or periodic optimizations may beperformed in response to real-time inputs regarding these and otherfactors.

In some embodiments, systems and methods disclosed herein can optimizejust-in-time delivery of material payloads via transporters from atiered network of supply nodes, to one or more demand nodes in order toreduce or prevent shortages of delivered material at the demand nodes,to improve or maximize the efficiency of the transporters, and/or toreduce or minimize excess stock at any point in the network. This can,for example, create two-pronged value for users. First, it can reduce orprevent a shortage of delivered material at destination sites, whichwould cause production operations at the destination site to cease. Thisis also known as non-productive time and can have a hefty penalty foreveryone involved in the operations. Second, it can reduce or minimizethe time spent by transporters (e.g., at any point in the network)responsible for, e.g., carrying, loading, and delivering the payloads.In some embodiments, such systems and methods enable a) a reduction indemurrage/detention (waiting) charges and/or b) an increase in loads perday for the transporter, hence increasing their utilization rate. Theseand other advantages are described further herein.

In some embodiments, systems and methods disclosed herein canautomatically reconcile various data (e.g., sensor data, customer data,financial data, billing data, shipment data, transporter data, etc.)associated with the payload delivery. In one example, payloads can bepurchased or rented and delivered by the same vendor. In anotherexample, payloads can be purchased or rented by one vendor (e.g.,payload vendor) and delivered by a different vendor (e.g., logisticsvendor). In some embodiments, this separation of the vendors between thepurchase/rent and the delivery of the payloads can lead to two sets ofdata from the two separate vendors. An automatic reconciliation of thesedifferent sets of data, for example, can efficiently reconcile the datawith each other and reduce the need for human manual approvals. Thiscan, for example, create value for users and customers as it can reducelabor and provide more accurate billing information more quickly, whichcan also result in more rapid payment for services rendered. These andother advantages are described further herein.

FIG. 1 shows a logistics system such as a resource transportation systemaccording to an exemplary embodiment. For illustrative purposes, anexemplary application of logistics system 100 is shown for sandlogistics. Logistics system 100 may comprise one or more supply nodes101. In this example, supply nodes 101 may comprise one or more distantmines 102 such as distant mines A, B, and C and/or one or more localmines 105 such as local mines D and E. Logistics system 100 may furthercomprise one or more intermediary supply nodes 103. The intermediarysupply nodes 103 may, for example, include one or more transloads 104.For example, the logistics system 100 includes transloads A, B, and C inthis example. Logistics system 100 may also include one or more demandnodes 106. The demand nodes 106 may include wells 107, such as wellsA-H.

In some embodiments, transports such as rail cars 108 may be used fortransporting a payload such as sand between supply nodes 101 (e.g.,distant mines 102) and intermediary supply nodes 103 (e.g., transloads104) via pathways 109. Transports such as trucks 110 may be used fortransporting a payload such as sand between intermediary supply nodes103 (E.g., transloads 104) and demand nodes 106 (e.g., wells 107).Transports such as trucks 110 also may be used for transporting apayload such as sand between supply nodes (e.g., local mines 105) anddemand nodes 106 (e.g., wells 107).

Continuing with the example of sand logistics, in some embodiments, thelogistics system 100 optimizes for just-in-time delivery of sand duringthe process of fracturing/stimulating a well. The process offracturing/stimulating a well may entail pumping a combination of sand,water and chemicals into the ground to fracture the reservoir andliberate oil and gas to flow to the surface. In this example, thelogistics system 100 can deliver the material or payload (e.g., sand)just-in-time via transporters such as rails 108 and trucks from a tierednetwork of supply nodes 101 (e.g., mines 102 and local mines 105) andintermediary supply nodes (e.g., transloads), to eventually one or moredemand nodes (e.g. wells 107). In some embodiments, the logistics systemreduces or prevents shortages of sand at the wells 107, increases ormaximizes the efficiency (e.g., loads per day) of the rails 108 andtrucks 109, and reduces or minimizes excess sand stock at the well 107at any point in the network.

In some embodiments, sand and/or other materials may, for example,comprise the payload. A mine may, for example, be a source pick-up pointfor materials. A well may, for example, be a drop-off point for sand.Hydraulic fracturing (“frac”) operations may, for example, be anactivity that consumes sand at the destination (well) site. A transloadmay, for example, be a facility where transport such as rail cars dropoff sand, and then transports such as trucks pick up that sand todeliver to the well. A transload may be a source of sand, similar to amine, however it is an intermediary source because the sand at thetransload comes from another mine, e.g., through rail. A node may be aprimary sand source (e.g., mines), an intermediary sand source (e.g.,transloads), as well as a destination site (e.g., well).

In some embodiments, systems and methods disclosed herein may includeone or more users. In some embodiments, the users may include one ormore customers who use the systems and methods disclosed herein. In someembodiments, the users may include one or more vendors (e.g., logisticsvendors and/or payload vendors) who use the systems and methodsdisclosed herein. In some embodiments, the users may include one or moreoperators (e.g., drivers) of a transporter. In some embodiments, theusers may include one or more supervisors who coordinate some or allsource and destination activity. In some embodiments, the users mayinclude one or more dispatchers who dispatch transporters to satisfy asupervisor's needs related, e.g., to pick-up times, drop-off times,quantities and routes taken by transporters. In some embodiments, theusers may include one or more coordinators who are in the field whoenter observable information into the system. In some embodiments, theusers may include one or more of each of the forgoing examples of users.

Continuing with the sand logistics example of FIG. 1, in someembodiments, the logistics system 100 may operate with one or moreusers. In some embodiments, the users may include one or more logisticssupervisors. Logistics supervisors who head operations and areresponsible and accountable for the timely delivery of sand may bereferred to as last mile owners (LMOs). LMOs may be at or near the topof a logistics operations hierarchy. LMOs may work for the company thatis fracking the well, which may be an oilfield services (OFS) company.In some embodiments, the users may include one or more transportoperators. Transport operators of a transporter may, for example,include truckers, rail operators, and/or other drivers and operators.Such transport operators may, for example, work for the trucking companyor be independent contractors. In some embodiments, the users mayinclude one or more transport dispatchers. Transport dispatchers whodispatch the operators of the transport (e.g., rail or truck) to satisfythe LMOs needs related to picking up and dropping of sand, pick-uptimes, drop-off times, quantities and routes taken by driver may bereferred to as trucking dispatchers and rail dispatcher. Truckingdispatchers and rail dispatchers may, for example, work for the truckingor rail company. In some embodiments, the users may include one or moresand coordinators. Sand coordinators who are onsite at the source ofsand may be referred to as sand mine or transload coordinators. Sandcoordinators may, for example, work for a company supplying the sand(e.g., the mine). In some embodiments, the users may include one or morewell coordinators. Well coordinators who are onsite at the destination(e.g., a well) may, for example, be referred to as onsite sandcoordinators, onsite well coordinators, frac engineers, or company menor women, or sand pushers. Such well coordinators may work for thecompany that is fracking the well, which may be an oilfield services(OFS) company.

Although this and other examples illustrated herein discuss sandlogistics for illustrative purposes, it will be appreciated that thepayload could be any other resource or combination of resources, such aschemicals, water, raw materials, finished goods, parcels, or anythingelse that is transportable, such as equipment and personnel, and othermaterials. Likewise, the nodes need not be mines, transloads, and wells.The types of nodes will depend on the applications. For example, in thecase of finished goods, the source nodes may, for example, includefactories, the intermediary nodes may, for example, include distributioncenters, and the demand nodes may, for example, include retail stores.As another illustrative example, in the context of chemicaldistribution, the source nodes may, for example, include chemicalmanufacturing plants, the intermediary nodes may, for example, includetemporary chemical storage facilities, and the demand nodes may, forexample, include chemical customers, such as mines, factories, andothers. Similarly, transporters are not limited to rail cars and trucks.In some embodiments, transporters may include other forms of transport,such as planes and boats, for example.

FIG. 2 shows a graph 200 that illustrates the impact of shifting sanddemand and supply on on-site storage at a well according to an exemplaryembodiment. Line 201 shows sand consumption per hour. Line 202 showstotal sand consumption. Line 203 shows the rate of sand delivery. Line204 shows the quantity of sand on-site.

A logistics system according to some embodiments may synthesizeinformation (e.g., information shown in the graph 200 with lines201-204) to improve or optimize schedules based on real-time and/orpredictive factors. For example, as shown in FIG. 2, the sandconsumption rate 201 varies over time and periodically drops to zero. Inthe context of fracking, the sand consumption rate 201 may at times dropto zero as part of the fracking process. For example, fracking mayinvolve repeated steps alternating between drilling holes and pumpingsand. The sand consumption rate 201 may periodically drop to zero duringdrilling and then resume during pumping phases. For example, the sandconsumption rate 201 drops to zero at time three and then rises againfrom times 3 to 5. The sand consumption rate may also drop to zero forlonger periods of time, for example, due to equipment malfunctions, asshown, for example between times 6 and 9. Such dips in sand consumptionmay have been unplanned and may result in non-productive time, e.g.,while machinery is being repaired.

Another factor is the sand stored on-site, shown with line 204. If line204 rises too high, it may reach the maximum storage capacity at aparticular site. In this case, transports (e.g., trucks) that wish todeliver sand may be unable to do so until additional sand storagebecomes available (e.g., by consuming some of the stored sand). This canresult in transports (e.g., trucks) being unproductive while they waitfor storage to become available so that they can deliver the sand. Onthe other hand, if the on-site storage drops to zero, there may not besufficient sand available to achieve the desired consumption rate. Thistoo can result in non-productive time while fracking operations awaitdelivery of additional sand. Therefore, it may be desirable to keepon-site sand 204 below the maximum, while still having sufficient sandto avoid sand shortages.

For example, consumption rate may, for example, be 100,000 pounds perhour. In this example, it may be desirable to have a delivery rate105,000 pounds per hour to match demand while maintaining a safetyfactor of 5000 pounds per hours. The safety factor at location maydepend on expected future demand for sand. In some embodiments, thesafety factor may range from 0% to 50%. In some embodiments, the safetyfactor may range from 5-50%. In some embodiments, the safety factor mayrange from 5-10%. In some embodiments, the safety factor may be about5%. In some embodiments, one or more safety factors may be based onfactors such as predicted supply and/or predicted demand.

As a further illustrative example, sand could be optimized by one ormore factors such as onsite storage. For example, maximum storage couldfor example be 3 million pounds at a particular well (though the amountof onsite storage could be more or less than this amount in otherexamples). Sand storage at the well could be maintained at a targetlevel (e.g., 75%, 50%, 25%, 10%, 5% of maximum onsite storage). In someembodiments, onsite storage targets may be dynamically adjusted. Forexample, a site storing 100 hours of sand on site a predictedconsumption rates, could bring it down to 5 hours, and then to 1 hour asthe mine operator prepares to shut down the mine.

In some embodiments, the logistics system may improve or optimize theonsite storage of sand through real-time monitoring and/or predictiveforecasting of the supply and consumption rate for sand at variouswells. The logistics system may improve or optimize the delivery of sandby adjusting the sand delivery rate 203 to maintain a desired level ofonsite sand 204 based, for example, on real-time and/or predicted sandconsumption 201. In some embodiments, the logistics system can performone or more of monitoring real-time information of the present andpredicting/optimizing information for the future.

The logistics system may also improve or optimize sand logistics nearthe end of operation for a well. When a well is approaching its end ofoperation, it may be desirable to use up the remaining on-site sand,while avoiding sand shortages. To help achieve this objective, the sandlogistics system can optimize factors like the sand consumption rate201, the total consumption 202, the sand delivery rate 203, and the sandon-site 204 to determine the remaining sand that is expected to be usedduring the operation of the well and how much additional sand should besupplied and at what rate to provide sufficient sand to completefracking operations at the well, without running out of sand and whilereducing or minimizing unused sand that remains at the well afterfracking operations are completed.

In some embodiments, by integrating data regarding transporteravailability, payload availability, and demand, the logistics system cangenerate both a simulation of the current scenario and suggestedscenarios including an optimized schedule for some or all wellsindicating improved or optimal transporters, payloads and supply nodesto be selected, as explained further herein. Scenarios may include oneor more optimizations of factors such as sand supply, sand demand, andtransporters. For example, a scenario might include increasing sandsupply at particular mines, adding additional mines, adding additionaltrucks, and rerouting trucks. In some embodiments, the systems mayprovide aggregate statistics for each scenario which serve to indicatethe quality of the scenario. For a current scenario, this can indicatepotential issues with the logistics environment (e.g., aggregatenon-productive time (NPT) resulting from sand shortages, aggregatedemurrage times). For an optimized scenario, this can indicate costsavings or loads per day efficiency improvements above the currentscenario.

FIG. 3 shows an example of value created for various stakeholders by thelogistics system according to an exemplary embodiment. Graph 300 shows agraph of plot of truck quantity per shift. Line 301 illustrates aconventional delivery process that uses a dedicated fixed number (e.g.,10) of trucks, picks up sand from pre-defined, fixed mines, and rotatesfixed quantities of trucks every Electronic Logging Device (ELD) shift.For example, line 301 illustrates an example of a conventional processwith 10 trucks per shift over 30 shifts for a total of 300 truck shifts,which amounts to 1.2 loads per day per truck.

In some embodiments disclosed herein, the logistics system may performintelligent scheduling. Intelligent scheduling may include intelligentlyassigning the least amount of trucks by staggering truck schedules andleveraging mines with the least wait and transit time. In someembodiments, the logistics system may monitor last mile real timehealth, which may comprise monitoring multi-mine/well/truck networkhealth (e.g., live operations status and wait times). In someembodiments, the logistics system may monitor last mile real timeoptimization, which may comprise providing real time actionable insightto improve health by changing mines (e.g., pick-up locations), truckquantity, and wells (e.g., drop-off locations).

Line 302 shows a delivery process optimized according to an exemplaryembodiment of the logistics system. By matching payload (e.g., sand)supply to demand by varying the number of trucks used per shift, thetotal number of truck shifts is reduced to 200 in this example and theefficiency is improved to 1.8 loads/day/truck. The logistics systemaccording to an exemplary embodiment can also reduce non-productive timeby reducing or avoiding payload (e.g., sand) shortages. For example, atpoints 303 where non-productive time is prevented, the logistics systemoptimizes the number of trucks based on the increase in demand, as shownwith line 302, whereas the conventional process continues to supply aconstant and temporarily insufficient number of trucks. The conventionalprocess 301 also results in demurrage 304, in which excess trucks arenot utilized due to insufficient demand.

Continuing with the exemplary application to sand logistics, thelogistics system according to an exemplary embodiment can create valuefor the trucking vendor, the end-user of the sand, and the suppliers ofsand. These values may, for example, include the following.

Improved Asset Utilization—For example, reducing the time a truck iswaiting to load and unload results in a lower total cost fortransportation for the end user of the sand and allows the truckingvendor to perform more loads during a given day.

Reduction or Prevention of Sand Drought—For example, by forecasting thedemand and availability of sand, the end user is better able to predictand react to any issues with supply which prevent the well from runningout of sand and stopping work.

Improved Visibility in Spot Market for Transporter and Sand Provider—Bygenerating improved or optimized schedules for both sand andtransporters and improving visibility into demand, both the truckingvendor and the sand vendor are able to more efficiently allocate theirsupply of sand and trucks and determine how many resources they haveavailable at any given time for offer on the spot market.

Continuing with the illustrative example shown in FIG. 3, the value tothe last mile owner includes two to zero instances of NPT on a two wellpad for sand avoiding NPT of 36 hrs, lower demurrage on a two well padfor a wait time drop of 3.4 hours, and lower average truck need per dayfor a two well pad resulting in a truck demand reduction of 34% and aloads/day increase of 50% in this example. The value to the truckingcompany in this example includes a higher revenue/utilization rateincrease of 32% and a loads/day increase of 50%, as well as lower driverturnover with an onboarding cost reduction of 50%. The value to the sendcompany in this example includes higher revenue/utilization rate,resulting in NPT savings and increased revenue from real-time spotmarket calculations, for example, increasing revenue by 20%.

FIG. 4 illustrates a logistics system 400 according to an exemplaryembodiment. The logistics system 400 may include one or more real-timesensors 401 and/or one or more real-time modules 402. For example,continuing with the illustrative example of sand logistics, thereal-time sensors 401 may comprise one or more well site sensors 403,fracking van sensors 404, sand mine sensors 405 a, transload sensor 405b, truck ELD sensors 406, traffic sensors 407, and/or others sensors408. The real-time modules 402 may comprise one or more ingest apps 409,logistics apps 410 (including, e.g., overall logistics modules, dispatchlogistics modules, and update logistics modules), and/or driver apps411. The logistics system 400 may also include one or more services orcloud platforms 412. The sensors 401 and modules 402 may interact withone or more physical servers and/or cloud-based servers 411 to uploadtheir inputs so that the system can process them. Such upload may occurautomatically in real time, may occur according to a predeterminedfrequency or schedule, may be triggered by some criteria (e.g. a percentchange in the value observed), and/or may be pushed or pulled fromsensor to server.

The logistics system 400 may use the real-time sensors 401 and/orreal-time modules 402 to match supply with dynamically changing demand,for example, by providing inputs to setup the operations as well as inreal time during operations. The inputs may come from sources including,for example, sensors that provide information about states of nodes inthe network (e.g., real-time sensors 401) and users using computer-basedmodules which provide similar information (e.g., real-time modules 402).

In some embodiments, the logistics system 400 may comprise one or moresand demand sensors such as well site sensors 403 and fracking vansensors 404. The sand demand sensors may indicate sand depletion, levelsof present and future sand at well sites (e.g. coming from fracking(frac) vans or silos). For example, sand level sensors 403 for storageat a well may include one or more of the following sensors: non-contactradar which performs continuous measurement, acoustics-based “3D”sensors, mechanical “bob” sensors which lower to meet level of sand asit is consumed, guided wave radar sensors, and/or laser level sensors.Additionally, for example, Frac Van sensors 404 include one or more ofthe following sensors: a computer system which contains the schedule offrac operations, highly accurate transducers which are placed alongsurface lines and in downhole locations which monitor real-timepressure, flowmeters placed along surface lines which measureconsumption rates of sand, densometers which measure the density of thefluid being pumped downhole.

In some embodiments, the logistics system 400 may comprise one or moresite operation status sensors such as site sensors 403, fracking vansensors 404, sand mine sensors 405 a, and/or transload sensors 405 b.The site operation status sensors may indicate operational status ofwells, mines and transloads.

In some embodiments, the logistics system 400 may comprise one or moresupply sensors such as sand mine sensors 405 a and transload sensors 405b. The sand supply sensors may indicate present and future inventorysand at mines and transloads (e.g., coming from silos and other sandstorage devices). For example, sand mine sensors 405 a and transloadsensors 405 b may include one or more of the following sensorsnon-contact radar which performs continuous measurement, acoustics-based“3D” sensors, mechanical “bob” sensors which lower to meet level of sandas it is consumed, guided wave radar sensors, and/or laser levelsensors.

In some embodiments, the logistics system 400 may utilize sandtransportation supply data. The logistics system 400 may obtain sandtransportation supply data from one or more transport availabilitysensors (e.g., truck ELD sensors 406), transportation availability datafrom other logistics systems, and/or sensors that indicate operationstatus of trucks and rails. The transport availability sensors mayindicate availability of truckers (e.g., using electronic loggingdevices (ELD) in trucks). The transportation availability may, forexample, be obtained via interfaces to logistics systems used bytrucking companies or rail companies. For example, truck ELD sensors 406may be certified by regulatory entities and may provide one or more ofthe following: connections to a truck's engine to record if a truck isin motion; driver status such as on-duty, off duty; and/or data instandardized format that be transmitted, e.g., via internet or cellulartechnology.

In some embodiments, the logistics system 400 may utilize sand movementdata. The logistics system 400 may obtain sand movement data from one ormore traffic sensors 407 and/or other sensors 408. For example, thelogistics system 400 may obtain sand movement data from one or moreinterfaces with systems that publish near real-time traffic data (e.g.Google Maps) for understanding status of payload movement in trucks,from systems that generate waypoint data to discover ETA, trafficcongestion and wait times at mines and transloads (e.g., using a driverapp), from barcodes read by scanners at multiple points along thenetwork of mines and wells, and/or from interfaces to systems thatprovide weather data which can be used to determine impacts of weatheron transportation. Traffic sensors 407 may for example be mounted in theproximity of roadways which produce data that is transmitted (e.g., viainternet or cellular technology) and may include one or more of thefollowing: radar, active infrared, lasers, and/or drones. Trafficsensors 407 may also be comprised of waypoint data which comes from adriver app (e.g., driver app 612 shown in FIG. 6). These waypoints maybe generated by the mobile device GPS system which is accessed via anapp. Other sensors 408 may for example include one or more of thefollowing: data from weather Sensors such as rain gauges or wind gaugeswhich detect adverse weather situations and transmit that data viainternet or cellular technology; and/or data from fleet systems thatrail and truck companies use to manage their assets, which may utilizeELD and mobile apps to determine trucker location, availability, speed,equipment, and other information.

In some embodiments, the logistics system 400 may include one or morereal-time modules 402. The real-time modules 402 may be, for example,mobile and/or web apps (applications) that allow users to input data.The real-time modules 402 may include one or more ingest apps 409 (alsoreferred to as job set up apps). For example, an ingest app 409 modulemay be used by last mile owners (LMOs) to enter data about the specificsof frac operations, including, for example, sand pick up sources (e.g.,mines and transloads), mine and transload quotas and contracts, sanddrop off destinations (e.g., wells), rates of types of sand (e.g., mesh)consumption during the frac operations (e.g., frac design), number ofrails and trucks, and other specifics of the frac job. Data from ingestapp 409 may be used to set up boundary conditions for logistics systemsoftware for a specific frac operation.

In some embodiments, the real-time modules 402 may include one or morelogistics apps 410 (including, e.g., overall logistics modules, dispatchlogistics modules, and update logistics modules). Logistics apps 410 mayinclude logistics health, diagnostics, and optimization modules, whichmay, for example, be used by LMO, trucking dispatch, sand mine /transload coordinators, on-site mine/transload/well coordinators tomanage and observe changes to all logistics involving delivery of sandfrom mines and transloads to wells. Logistics apps 410 may be used tosee failure points in the network, see predicted consequences of thefailure points, generate automated optimization schedules andrecommendations, and act upon optimizations to reduce or eliminate NPTor demurrage.

In some embodiments, logistics apps 410 may perform rail and truckdispatching. This feature may be used by truck dispatchers and raildispatchers to schedule and assign trucks and rails to satisfy aschedule that is desired by an LMO. Logistics apps 410 may also providea network status. This feature may be used by coordinators to crowdsource information about progress of frac operations at a destinationsite. This may include, for example, levels of sand in well sitestorage, consumption rates of sand, arrival and/or departure of trucksand/or rail transports, status of wells, mines and transloads, and/orthe like.

In some embodiments, the real-time modules 402 may include one or moredriver apps 411. The driver apps may be used by drivers of trucks andrails. Driver app modules may communicate location information about atransporter and may also be used to communicate with a driver regardinga job they are to perform.

In some embodiments, the logistics system 400 may use data analyticsbased on historic data. For example, the historical data may includedifficulty or likelihood of well issues (e.g., those impacting theschedule or rate of sand consumption, such as from well equipmentfailure). Historical data may, for example, be based on factors such asfrac crew experience, geological formation depth and criteria, and/orhistorical performance of equipment in this geological formation.Real-time data obtained from one or more real-time sensors and/orreal-time modules may be stored for later use as historical data.Historical data may also, for example, be obtained from third partysystems and other data sources. In some embodiments, the logisticssystem may utilize a combination of real-time data and historical data.In some embodiments, one or more aspects of the logistics system 400(and other logistics systems described herein) may be implemented withone or more computing devices such as mobile devices, laptops, desktops,cloud computing resources, servers, terminals, virtualization tools,communication devices. In some embodiments, one or more components oflogistics system 400 may include one or more transmitters, receivers,and/or transceivers to communicate using one or more techniques such aswired communication and/or wireless communication. In some embodiments,one or more such computing devices of the logistics system may comprisesoftware such as one or more applications or apps. In some embodiments,a logistics system may include one or more hardware and/or softwarecomponents. In some embodiments, one or more components of a logisticssystem may include one or more memories storing instructions and one ormore processors configured to execute the instructions to perform one ormore operations described herein. In some embodiments, one or moresoftware components of a logistics system may be embodied on anon-transitory computer readable medium.

In some embodiments, a resource transportation system (e.g., a logisticssystem) may comprise a production operation at a first location such asa well 107 (e.g., well A) in FIG. 1. The production operation may use aresource (e.g., sand and/or other materials) at a use rate that variesover time (e.g., as shown with consumption per hour 201 in FIG. 2). Theuse rate may further depend on availability of other resources at afirst location such as a well 107. An amount sensor such as real-timesensors 401 in FIG. 4 at the production operation can be configured todetermine an amount of the resource at the production operation such assand on site 204 in FIG. 2. There may also be another a sourcerepository of the resource at a second location such as another well 107(e.g., well B) remote from the first location. A transporter such as arailcar 108 or a truck 110 can be configured to transport the resource.A location sensor (e.g., a real-time sensor 401) associated with thetransporter can be configured to determine a transporter location. Theresource transportation system may additionally include a control systemcommunicably coupled to the location sensor and the amount sensor. Thecontrol system may be further configured to receive information aboutthe use rate and to reroute the transporter to one of the first locationand the second location based on factors such as the amount, the userate, and the transporter location.

In some embodiments, a system for resource transportation (e.g., alogistics system) may comprise a routing command subsystem configured tobe communicably coupled to an amount sensor (e.g., real-time sensor 401in FIG. 4) at a production operation at a first location (e.g., a well 7in FIG. 1). The amount sensor may be configured to determine an amountof a resource (e.g. sand or other materials) at the productionoperation. The production operation may use the resources at a use ratethat varies over time (e.g., as illustrated with consumption per hour201 in FIG. 2). The use rate may further depend on availability of otherresources at the first location. The system for resource transportationmay also include a location sensor (e.g., real-time sensors 401 in FIG.4) associated with a transporter (e.g., a truck or rail). Thetransporter may be configured to transport the resource and the locationsensor may be configured to determine a transporter location. Therouting command subsystem may be further configured to receiveinformation about the use rate and reroute the transporter based on theamount and the transporter location.

FIG. 5 illustrates a logistics system 500 according to an exemplaryembodiment involving an illustrative example with two wells and twomines. The logistics system 500 may include mine A 501, mine B 502, wellA 503, well B 504, one or more trucks 505, and pathways 506 between themines and the wells. In this illustrative example, two frac operationsare being provided with sand from two separate mines. As discussedfurther below, this example is used to illustrate an example of inputsboth from sensors and modules and how logistics system 500 leverages oneor more algorithms and other techniques to improve or optimize logisticsoperations, according to embodiments. In some embodiments, inputs (e.g.,sensors and modules) provide data. The logistics system may include aprocess (e.g., which runs on a server) that uses data from the sensorsand modules to match supply with demand at all well sites taking intoaccount numerous constantly changing real time factors within thenetwork. It will be appreciated that the use of two mines and two wellshas been selected for illustrative purposes and that in otherembodiments and applications, the number of mines and wells may begreater or less than two.

FIG. 6 illustrates a logistics method 600 according to an exemplaryembodiment. The logistics method 600 will be described for illustrativepurposes with an exemplary application to using two mines and two wells,as shown in FIG. 5. At step 601, the job setup is performed. Job setupmay include the last mile owner (LMO) being made aware of a schedule ofimpending frac operations at two wells. The LMO may use a job setup appto enter information about destination wells and frac operations thatwill take place there. The sand logistics job may be setup based on pickup and pick up sources (e.g., mines and transloads), drop offdestinations (e.g., wells), rates of payload (e.g., sand) consumptionduring the frac operations (e.g., frac design), number of rails andtrucks, and other specifics of the frac job. FIG. 7 shows a screen fromthe job setup application according to an exemplary embodiment.

Returning to FIG. 6, at step 602, the logistics system performs aschedule generation step. The schedule generation step may comprisereceiving initial inputs from a job setup app received in step 601 andinitial inputs from sensors 603 and using an algorithm to create aschedule to support frac operations with pickup times at mines anddrop-off times at wells. Along with the schedule, the algorithm maydetermine and output non-productive time (NPT) and demurragecalculations. The schedule may be comprised of specifics about thesource and destination locations, the schedule of transporters includingpickup and drop-off times, and information about the different stages ofproduction operations. If those calculations are within acceptableranges, then the schedule may be sent (e.g., via email) to a last mileowner. FIG. 8 shows an example of an optimized schedule for one mine andone well according to an exemplary embodiment.

In some embodiments, the algorithm 602 may improve or optimize theschedule based on factors such as NPT, demurrage, and/or utilizing fewertrucks/rails. In some embodiments, the inputs to algorithm 602 mayinclude one or more of the following at one or more wells: Number ofstages at well; Latitude/Longitude of well; Prefill Date; Frac Date;Total number of trucks available; Unload Time; Truck Type; Truck Load;24-hour demurrage threshold; Storage capacity by mesh type; and/or Minesavailable to supply this well. In some embodiments, the outputs ofalgorithm 602 may include one or more of the following: a schedule fortrucks (e.g., shown in FIG. 8), a schedule for sand needed by stage(e.g., shown in FIG. 8), optimized pickup and drop-off locations, and/orschedule of rails. In some embodiments, the algorithm may optimizefactors such as NPT, demurrage, and/or trucks/rails utilization based onone or more of the following: Number of trucks/rails, Pickup locations,and/or drop-off locations. In some embodiments, the algorithm 602 takesthe inputs and creates a schedule of trucks and rails that result in thelowest numbers for NPT, Demurrage and Truck utilization. In someembodiments, the algorithm 602 does not assign trucks to jobs andtherefore is not aware of constraints that may appear due to trucksbeing assigned by dispatch. In some embodiments, these considerationsmay be addressed at steps 607 and 609. Dispatchers may take the outputschedule from this algorithm and assign trucks using a dispatch tool. Insome embodiments, algorithm 602 may use one or more computing techniquessuch as extrapolation techniques, interpolation techniques, artificialintelligence methods, machine learning, data set comparisons,reinforcement, supervised learning, unsupervised learning, neuralnetworks, Bayesian networks, and R2 convergence techniques.

Continuing with FIG. 6, at step 612, the logistics system according toan exemplary embodiment dispatches transportation. For example, the LMOcan work with a rail and trucking dispatcher (or multiple dispatchers,e.g., if multiple trucking companies are used) to assist transporterswith adhering to the schedule. The dispatcher uses a dispatch module toassign trucks/rails to adhere to the schedule. Truck drivers receiveassignments on a driver module indicating where they are to proceed andwhen. FIG. 9 shows a screenshot from a dispatch module according to anexemplary embodiment. FIG. 10 shows a screenshot of a driver moduleaccording to an exemplary embodiment.

Continuing with FIG. 6, at step 606, the logistics system according toan exemplary embodiment determines sand logistics current status in alogistics app. In some embodiments, apps described herein may run oncomputing devices such as mobile devices, computers, servers, cloudcomputing systems, and other computing devices. For example, driversbegin picking up material and delivering it. Then in real time the appleverages real time data from the sensors and user input in the app tocalculate the current status of the supply and demand of sand. Real timetransport sensors 605 a input the status of frac operations includingtruck driver availability, traffic situations, and/or the like.Real-time supply sensors 605 b provide real-time supply information,such as sand production rates at one or more mines, on-site storage atone or more mines or wells and/or transloads, among other supplyinformation. Real-time demand sensors 605 c provide information in realtime about resource demands, such as consumption rates at one or morewells, on site storage at one or more mines or wells, among other demandinformation. Mines and transloads or well sites may, for example, becomeinoperable and unable to support production operations, which is afailure point. Onsite coordinators can perform real time logistics appupdates 604 to update the logistics app with this status, as well asother data such as observed levels of materials, stages of progress andother aspects of frac operations. The logistics app can also receiveupdates from the driver apps (e.g., indicating optimizations that havebeen selected). The logistics system uses the real-time updates from thelogistics app, driver app, and the real-time sensors in step 606 todetermine the current sand logistics status.

Continuing with FIG. 6, at step 607, the logistics system makes futuresand logistics predictions and determines consequences. For example, alogistics app performs calculations to predict the future status of thesupply and demand of sand. The app also shows the consequences (e.g.,positive or negative) of the prediction by quantifying it into factorssuch as NPT and demurrage. At step 608, the logistics system determinesif the prediction (e.g., the NPT and demurrage prediction) isacceptable. If the predictions are acceptable, the method moves to step610. If the predictions are not acceptable, the method moves to step 609and performs an optimization algorithm. The algorithm can react to someor all of the above changes in inputs to the sand logistics predictionstep 607, including inputs from 604, 605, and 606. FIG. 17 illustratesan example of a logistics module indicating a failure at one or moremines that have deliveries scheduled for the listed wells and potentialNPT and demurrage impacts according to an exemplary embodiment. FIG. 18shows an example of a predicted impact on well site storage of sand fromthe change illustrated in FIG. 17 according to an exemplary embodiment.

In some embodiments, step 607 comprises a simulation algorithm andoutputs what predicted health of sand logistics is. In some embodiments,step 607 may keep levers static coming in from the real time sensors andmodules. The objective at 607 may include determining, based on currentinputs, how pickups/drop-offs will proceed based on the currentscenario. In some embodiments, the inputs to 607 may include may includeone or more of the following at one or more wells: Number of stages atwell; Latitude/Longitude of well; Prefill Date; Frac Date; Total numberof trucks available; Unload Time; Truck Type; Truck Load; 24-hourdemurrage threshold; Storage capacity by mesh type; and/or Minesavailable to supply this well. In some embodiments, the inputs mayinclude truck assignments that have been made in the dispatch module. Insome embodiments, the output of step 607 may include predicted NPT andpredicted demurrage. In some embodiments, step 607 may take thereal-time inputs (e.g., with or without modification) and run asimulation that indicates what NPT and demurrage will be if there are nochanges to the inputs. In some embodiments, algorithm 607 may use one ormore computing techniques such as extrapolation techniques,interpolation techniques, artificial intelligence methods, machinelearning, data set comparisons, reinforcement, supervised learning,unsupervised learning, neural networks, Bayesian networks, and R2convergence techniques.

In some embodiments, step 609 comprises an algorithm to optimizepredicted health of last mile logistics if NPT and demurrage are notacceptable. Step 609 may include one or more of the followingobjections: predicted NPT, demurrage, and/or utilizing least amount oftrucks/rails. In some embodiments, the inputs to step 609 may includemay include one or more of the following at one or more wells: Number ofstages at well; Latitude/Longitude of well; Prefill Date; Frac Date;Total number of trucks available; Unload Time; Truck Type; Truck Load;24-hour demurrage threshold; Storage capacity by mesh type; and/or Minesavailable to supply this well. In some embodiments, the inputs mayinclude truck assignments. In some embodiments, the outputs of step 609may include one or more of the following: a schedule for trucks (e.g.,shown in FIG. 8), a schedule for sand needed by stage (e.g., shown inFIG. 8), optimized pickup and drop-off locations, schedule of rails,predicted NPT, and/or predicted demurrage. In some embodiments, thealgorithm may optimize factors such as predicted NPT, demurrage, and/ortrucks/rail utilization using one or more of the following: number oftrucks/rails, pickup locations, drop-off locations. In some embodiments,step 609 may take the inputs and creates a schedule of trucks and railsthat result in the a reduced or the lowest numbers for NPT, Demurrageand Truck utilization. There are thresholds for acceptable levels of NPTand Demurrage (608) and the algorithm may re-run with different factorsif the results are below threshold. For example, if the threshold forNPT is 10 hours and the algorithm generates a schedule that results inNPT of 12 hours, it may re-run and attempt to raise the number oftrucks. Another example, if the threshold for demurrage is $8,500 andthe algorithm generates a schedule that results in a demurrage of$10,000, it will re-run and attempt to decrease the number of trucks. Itmay also try to send trucks to other pickup or drop-off locations. Insome embodiments, algorithm 609 may use one or more computing techniquessuch as extrapolation techniques, interpolation techniques, artificialintelligence methods, machine learning, data set comparisons,reinforcement, supervised learning, unsupervised learning, neuralnetworks, Bayesian networks, and R2 convergence techniques.

Continuing with FIG. 6, at step 610, the logistics system according toan exemplary embodiment performs predictive diagnostics and develops anoptimization plan for the logistics app. For example, the logistics apputilizes an algorithm to perform diagnostics and develop the optimizedplan. The algorithm may utilize advanced analytics and artificialintelligence to create a more efficient solution. The algorithm may alsoaddress failure points. For example, as discussed herein, variousfailure points occur during logistics operations. The logistics systemcan address such failure points by adjusting schedules and recommendingoptimizations to the LMO. In some embodiments, the logistics method 600can include various steps for one or more of real-time informationmonitoring of the present as well as prediction/optimization for thefuture. The logistics method 600, for example, can focus on steps forcollecting and analyzing real-time information of the present. Forexample, the logistics method 600 can perform steps 601, 602, 603, 604,605 a, 605 b, 605 c, 606, and 612 for collecting and analyzing real-timeinformation of the present.

FIG. 7 shows a screen from a job set up application 700 according to anexemplary embodiment. The application 700 allows a user to enteridentifying details of the location (e.g. Name 701, Latitude 702 in FIG.7) and demand details outlining the schedule of consumption of sand forthat location (e.g. Prefill Date 703, Total # of Stages 704, Frac Date705, Unload Tim 706, Truck Load 707), storage units on location andtheir capacity (e.g., to determine the maximum amount of sand that canbe held at the well pending consumption) (e.g. storage 708), and themines/sand supply points that this location has contract agreements withand is allowed to pull sand from, if desired (e.g. mines 709).

FIG. 8 shows an example of an optimized schedule for one mine and onewell according to an exemplary embodiment. FIG. 8 shows a screen 800from an app that may be the output of step 602 from FIG. 6, showing aschedule for the frac operation. In this example, the schedule 801 isshown for well Dodger 18L but the schedule can include multiple wells.The schedule includes scheduling information 802, which can indicate,for example, how many trucks should be used in the frac, what mine theyshould pick up their loads from, what type or mesh of load and what timethey should depart. Well information 803 may include details of the wellname and location. Sand information 804 may include a type of sand“mesh” in addition to details on various stages of the frac taking placeat the well. Mine information 805 may also include information about amine that is the source of the sand.

FIG. 9 shows a screenshot from a dispatch module according to anexemplary embodiment. FIG. 9 shows a dispatching app 900 such asdispatching app 410 in further detail. Dispatching app 900 may be usedby a trucking company dispatcher after the dispatcher has received aschedule (e.g., detailed in FIG. 8). The dispatching app 900 presentsthe user with a list of available truck drivers 901 and it assists thetruck dispatcher with assigning drivers to both wells and mines. Thefirst column 902 lists out known active wells. A user can assign a truckdriver to a well using interface 903. Additionally, the user can assigntruck drivers to mines. The list of mines (e.g., 804 in FIG. 8) showssome or all truck drivers that have been associated with each mine, asshown for example at interface 905.

FIG. 10 shows a screenshot of a driver module according to an exemplaryembodiment. FIG. 10 shows a driver module 1000 (e.g., from the driverapp 411 in FIG. 4). Driver module 1000 displays on a map 1001 of adestination that a driver should travel to for completion of work. Italso shows a text display 1002 of current work that the driver iscompleting and allows the driver to press a button 1003 to receivedirections to the destination.

An LMO may observe progress via a logistics module, as illustrated inFIGS. 11-16. FIG. 11 shows a screenshot from the logistics moduleindicating the status of multiple wells, their associated mines, and thestatus of NPT and demurrage for each as related to a pre-definedalerting threshold according to an exemplary embodiment. FIG. 11 showsthe “Wells” screen 1100 from a logistics app (e.g., logistics app 410 inFIG. 4). The logistics app may include, e.g., four tabs 1107 along thebottom and when the “wells” tab is pressed, the user is brought to the“Wells” Screen 1100. Rows are listed which show individual wells (e.g.,wells 1101, 1105). Each row lists the well name (in the case of well1101 it is “Industrious Well 18A”) and status (e.g., “operational” forwell 1101). Below the name and status are 4 boxes showing differentinformation about the well (e.g., icons 1102, 1103, 1104 and 1105). Thefirst box 1102 shows whether the well is operational or not. If it isoperational, a green checkbox appears, if it is not operational, a red“X” appears in a circle. The second box 1103 indicates the state of thesand supply that is being used to sustain operations at the well. If thesand supply is adequate to support operations, a green checkbox appears,if it is not adequate, a red “X” appears in a circle. In the example in1103 the sand supply is not adequate because a red X appears. Box 1104shows whether NPT (Non Productive Time) is considered acceptable or not.If NPT is acceptable then a green checkbox appears, if it is not then ared “X” appears in a circle. Box 1105 shows whether demurrage isconsidered acceptable or not. If demurrage is acceptable, then a greencheckbox appears, if it is not acceptable then a red “X” appears in acircle. Users can click the edit icon 1106 to edit parameters of thewell and re-run optimizations 609 to attempt to avoid unacceptablesituations.

FIG. 12 shows a screenshot from the logistics module indicating theactual and predicted status of on-site storage at a well according to anexemplary embodiment. FIG. 12 shows the well detail screen 1200. Thisscreen within the logistics app may be reached by tapping on one of thewells shown in FIG. 11. In this example the well 1201 that has beendetailed is “Sam Well 1A”. A brief list of status items 1201 is shownindicating whether the well is up or down, whether there is any NPT orDemurrage, for example. Two tabs 1202 are shown allowing the user toview supply of sand to that well by mesh type (e.g., 100 or 40/70 inthis example). Clicking on one of the meshes shows bar charts 1203indicating status of that sand by frac stage. The bars indicate safetystock levels in the darker color and levels beyond safety stock inlighter color above the darker bars. For any stage, detail 1204 is shownon that stage when it bar is tapped by the user. This screen allows theLMO to look forward and predict how what sand levels will be as the fracprogresses.

FIG. 13 shows a screenshot of a logistics module indicating theavailable mines or transloads along with their status and current waittimes according to an exemplary embodiment. FIG. 13 shows the minescreen 1300. This screen may be displayed by a logistics app and may bereached by tapping on the mines tab at the bottom of the logistics app(e.g., as shown in FIG. 11). The mine screen 1300 lists some or allknown mines (e.g., mines 1301 and 1302) and their current status alongwith known wait times at each mine. In this example, Priyesh Mine 1 isexperiencing availability problems (indicated by exclamation point belowmine icon) and Shale Mountain is not experiencing problems (indicated bycheck mark below mine icon). Users can click the edit button 1303 toedit information about the mines for recalculation in an optimizationalgorithm such as optimization algorithm 609.

FIG. 14 shows a screenshot of a logistics module's well/mine statusreporting according to an exemplary embodiment. FIG. 14 shows the reportscreen 1400. The user can report that a mine or well is experiencing astatus change (e.g., down or up) using this screen. If a well has had astatus change, the user may select well status 1401. If a mine has had astatus change, the user may select mine status 1402. Once a well or mineis reported to have a status change, an alarm may be sent to an LMO whoconfirms the alarm (e.g., as shown in FIG. 15).

FIG. 15 shows a screenshot of the logistics module's well/mine statusreporting according to an exemplary embodiment. FIG. 15 shows theconfirmation screen 1500 in which the LMO can confirm that a report ofmine or well status change (e.g., as shown in FIG. 14) is correct. TheLMO views the name of the mine or well 1501, and the informationincluded in the alert 1502, then selects the validate button 1503 buttonto validate the report.

FIG. 16 shows a screenshot from the logistics module indicating a mineestimated to be down for 56 hours according to an exemplary embodiment.FIG. 16 shows an example of a mine detail screen 1600, which showsdetailed information about a mine. In this example, the Priyesh 100 Mineis shown (e.g., selected via the interface shown in FIG. 13). A briefstatus 1601, e.g., including up/down and demurrage is shown at the topof the screen. Tabs 1602 are presented indicating meshes, in thisexample the two meshes shown are 100 and 40/70, and 100 has beenselected. The status for mesh 100 is that the mine is down, as indicatedby mine status 1603 and an expected time of 56 hours is shown until themine will be back up. Additionally, a window 1604 also shows expectedtruck wait times for all meshes and how many trucks are en route tomines at the moment.

FIG. 17 illustrates an example of a logistics module indicating afailure at one or more mines that have deliveries scheduled for thelisted wells and potential NPT and demurrage impacts according to anexemplary embodiment. FIG. 17 shows a well screen 1700 similar to thatof 1100 however in this example both NPT and Demurrage are shown to beat unacceptable levels in display area 1701 as evidenced by the “X”marks under both NPT and Dem in the first row. This is an indicator tothe LMO that the sand supply issues may cause unacceptable NPT andDemurrage impacts and that action should be taken to mitigate thissituation.

FIG. 18 shows an example of a predicted impact on well site storage ofsand from the change illustrated in FIG. 17 according to an exemplaryembodiment. FIG. 18 shows a mine screen 1800 similar to that of 1200 inFIG. 12, however in this example, the mine is predicted to experience asand shortage in stage 18 of the frac, as shown in display area 1801.The resulting shortage results in NPT and Demurrage as indicated in thetop of the screen 1802. This situation may be important for the LMO toresolve as real cost impacts may be rapidly realized.

FIG. 19 shows is a table 1900 demonstrating an example of how differentfailure points are detected (data), the failure points themselves, andexamples optimizations according to an exemplary embodiment. Row 1901illustrates an example in which a sensor receives frac van sensor dataindicating that sand is running low at a well (e.g., Well A 503 in FIG.5). In response, the logistics system according to an exemplaryembodiment can route more trucks to the well (e.g., Well A 503 in FIG.5). Row 1902 shows an example of a sensor detecting silo sensor dataindicating a first mine (e.g., Mine A 501 in FIG. 5) is down and alogistics app for a sand mine coordinator indicating a first mine (e.g.,Mine A 501 in FIG. 5) is down in a logistics module. In response, thelogistics system according to an exemplary embodiment can route trucksfrom a first mine (e.g., Mine A 501 in FIG. 5) to a second mine (e.g.,Mine B 502 in FIG. 5). Row 1903 shows an example in which waypoints froma driver app indicate long wait times at a first mine (e.g., Mine A 501in FIG. 5). In response, the logistics system according to an exemplaryembodiment can route trucks from a first mine (e.g., Mine A 501 in FIG.5) to a second mine (e.g., Mine B 502 in FIG. 5). Row 1904 illustratesan example in which waypoints from a driver app indicate long wait timesat a first well (e.g., Well A 503 in FIG. 5). In response, the logisticssystem according to an exemplary embodiment can route trucks from afirst well (e.g., Well A 503 in FIG. 5) to a second well (e.g., Well B504 in FIG. 5). Row 1905 illustrates an example in which a well sitecoordinator updates a logistics app indicating a first well (e.g., WellA 503 in FIG. 5) is down due to wireline issues. In response, thelogistics system according to an exemplary embodiment can route trucksfrom a first well (e.g., Well A 503 in FIG. 5) to a second well (e.g.,Well B 504 in FIG. 5). Row 1906 illustrates an example in which a sandmine sensor indicates one of the lanes ata first mine (e.g., Mine 501 Ain FIG. 5) is down. In response, the logistics system according to anexemplary embodiment can route trucks from a first mine (e.g., Mine A501 in FIG. 5) to a second mine (e.g., Mine B 502 in FIG. 5).

Continuing with the description of step 610 in FIG. 6, according to anexemplary embodiment, the logistics system can prepare an optimizationplan with recommendations for optimization steps (e.g., diverting trucksfrom one or more mines to one or more other times). A new schedule andoptimization recommendations may be sent (e.g., emailed, texted, orotherwise conveyed or communicated) to the LMO based on theserecalculations. Additionally, the users can be shown the predictivediagnostic and optimization recommendations. FIG. 20 shows a screen shotdisplaying suggested optimizations for an LMO according to an exemplaryembodiment.

In some embodiments, at step 611 in FIG. 6, the logistics system takesaction via the logistics app. The LMO can take action with other usersand communicate the new plan to the dispatcher. The dispatcher cancommunicate changes to the schedule to drivers using the app, asexplained herein. For example, if sensors detected a long wait time at awell, the LMO can accept the optimization recommendations to routetrucks to a different well, e.g., via the logistics app. The LMO cancommunicate this change of plan to the dispatcher who can in turncommunicate it to the trucks. The LMO may also communicate directly withthe transports (e.g., trucks). At step 612 in FIG. 6, the driver appsmay provide feedback on current driver status (e.g., via real timetransport sensors 605 a) for use in determining the sand logisticscurrent status for the logistics app in step 606.

As noted above, the forgoing example uses a scenario with two mines andtwo wells for illustrative purposes. However, there can be multiplesources (e.g., mines) for the payload (e.g., sand) with varioustransports (e.g., some served by rail, some by truck) and there can belarge numbers of destinations (e.g., wells) needing the payload (e.g.,sand). The sheer numbers of permutations and combinations of trying todeal with all the failure points creates extreme complexity. Thelogistics system according to some embodiments described herein scalesto address a large network of nodes to address this complexity. Anadditional example is provided here to illustrate how systems describedherein handle a case with vastly more complexity. FIG. 21 shows anexample of a logistics system with three distant mines which ship tothree different transload facilities, two local mines, and eight wellsconsuming the sand according to an exemplary embodiment. Paths 2101 and2102 illustrate an exemplary steady-state routing with no failurepoints.

FIG. 22 shows an example of a logistics system with three distant mineswhich ship to three different transload facilities, two local mines, andeight wells consuming the sand with multiple points of failure accordingto an exemplary embodiment. In the example shown in FIG. 22, one or morepaths 2201 and 2202 are interrupted by points of failure 2203 such as amine down, a well down, and a high truck wait time resulting ininterrupted supply paths 2204. Though each of the failure points may beindependent (and/or related), the complexity introduced by each failurepoint affects the system extensively, and the solution to plan aroundthe failure points can be very complex.

FIG. 23 shows a table of optimizations in response to detected points offailure according to an exemplary embodiment. Table 2300 shows how eachfailure point in FIG. 22 can result in optimizations according to anexemplary embodiment. As illustrated in row 2301, a sand mine sensor candetect that distant mine C is down. In response, the logistics systemcan perform an optimization to reduce or eliminate NPT and demurrage byre-routing rail traffic to other distant mines and/or changing drop-offlocations for sand to another local mine or transload and reroutingtrucks. As illustrated in row 2302, waypoints from a driver app candetect long wait times at transload station C. In response, thelogistics system can perform an optimization to reduce or eliminate NPTand demurrage by changing the pickup location for sand to another localmine or transload and rerouting trucks. As illustrated in row 2303, fracvan sensors can detect that well G is down. In response, the logisticssystem can perform an optimization to reduce or eliminate NPT anddemurrage by rerouting trucks to different wells.

FIG. 24 shows a conceptual representation of an optimization algorithm2400 according to one or more exemplary embodiments. The algorithm 2400may dynamically optimize factors such as NPT and/or demurrage usinginputs from one or more input devices (e.g., sensors and/or data entrydevices), such as demand input devices (e.g., demand-sensors or dataentry devices) 2401, supply input devices (e.g., supply sensors or dataentry devices) 2402, and transportation input devices (e.g.,transportation sensors or data entry devices) 2403. In some embodiments,the input devices such as demand input devices 2401, supply inputdevices 2402, and transportation input devices 2403 may provide realtime inputs and/or non-real time inputs. In response to inputs from oneor more input devices such as demand input devices 2401, supply inputdevices 2402, and transportation input devices 2403, a control systemcan dynamically adjust factors such as supply, demand, andtransportation. Continuing with the exemplary example of sand logistics,supply of a resource such as sand can be dynamically adjusted at one ormore mines and/or by adding or removing mines in response to inputs fromone or more sensors. The location of the sand supply can also bedynamically adjusted in response to inputs from one or more sensors. Forexample, if demand input devices 2401 detect a demand center istemporarily un-operational, the algorithm can re-route the transportersand sand nodes to different demand centers to reduce demurrage oftransporters. If the sand input devices 2401 detects the sand supplynode is operational, the algorithm can reroute transporters to differentsand supply nodes, to reduce demurrage for transporters and protect thedemand node from having NPT. If the transport sensors detect delay indelivery of sand to the demand node due to long wait time at supplynode, or traffic or other operational issues, the algorithm can suggestadding new transporters with the same or new supply nodes that arecloser to the demand node. If the sensors across the demand node suggestlower consumption of sand than planned, the algorithm can predictutilization rate of sand and transport nodes and allow the sand andtransport provider to sell their excess available resources in the spotmarket. The algorithm can also optimize NPT and/or demurrage whenreacting to any combination of sensors across the supply node, demandnodes, and transportation nodes.

In some embodiments, the algorithm may, for example, reroute atransporter based on supply and/or demand inputs (e.g., from an inputdevice such as a sensor or a data entry device). In some embodiments, adata entry device may receive automated inputs (e.g. from atransportation app, mobile device, computer, and/or one or more thirdparty systems) and/or manual inputs (e.g., from a driver or dispatcher).In some embodiments, sensors may receive automated and/or manual inputs.For example, a location sensor can automatically track locations (e.g.,of a transport) or may receive manual inputs (e.g., a QR code scannerthat is manually activated when a truck arrives or departs from alocation, for example). In some embodiments, inputs may be continuous orperiodic. For example, a transporter location may be trackedcontinuously (e.g., through some or all of a route) or periodically(e.g. at particular points, regularly and/or irregularly).

FIG. 25 illustrates a reconciliation method according to an exemplaryembodiment. An automatic reconciliation method 2500 may includereceiving an order 2502 of payloads from a customer. The customer, forexample, may submit the order 2502 via logistics system software or anyother software comprising one or more applications or apps. FIG. 27shows an exemplary payload ordering app 2702, which can be a part of alogistics system 2700. The customer, for example, may use the payloadordering app 2702 to submit the order 2502. In another example, thecustomer may use the logistics app 410 to submit the order 2502. In someembodiments, the payload ordering app 2702 and the logistics app 410 maybe embodied together in one or more apps, which may operate on one ormore devices. In some embodiments, the customer may place the order 2502via email, via application programming interface (API) integration, orvia any other electronic transmission. In some embodiments, the customermay place the order 2502 using traditional means such as viatelephone/facsimile, via paper mail, or place an order in person. Insome embodiments, once the order 2502 has been place, informationassociated with the order 2502 can be updated to the logistics system.

In some embodiments, the customer may submit the order 2502 based onreal-time updates of the present conditions and/or based on thepredictive diagnostics and optimized plan for the future, for example,based on any components or features within the logistics system 400 orthe logistics method 600, and/or based on any other informationavailable to the customer. In some embodiments, the order 2502 may beautomatically submitted (e.g., via the payload ordering app 2702 and/orthe logistics App 410) based on real-time updates of the presentconditions and/or based on the predictive diagnostics and optimized planfor the future, for example, based on any components or features withinthe logistics system 400 or the logistics method 600. For example, asdiscussed herein, the logistics system or method can detect variousfailure points of the logistics operations. The logistics system ormethod can, not only address such failure points by adjusting schedulesand recommending optimizations, but also, for example, by recommendingthe customer to submit the order 2502. In some embodiments, logisticssystem or method may address such failure points by automaticallysubmitting the order 2502, for example, based on the preference of theusers, based on the urgency of addressing such failure points, and/orbased on the level of consequences of failing to address such failurepoints.

In some embodiments, as illustrated in FIG. 25, the order 2502 ofpayloads may be fulfilled by multiple vendors. For example, the payloadsmay be delivered by Vendor A (e.g., logistics vendor), but the payloadsmay be sold/rented by Vendor B (e.g., payload vendor). It will beappreciated that the use of two separate vendors (e.g., Vendor A andVendor B) has been selected for illustrative purposes and that in otherembodiments and applications, a customer's order of payloads may befulfilled by one single vendor or fulfilled by more than two vendors.

Continuing with FIG. 25, at step 2504, the logistics system may routethe order 2502 of payloads to Vendor A (e.g., logistics vendor) who mayplan the logistics for the delivery of the payloads. In someembodiments, Vendor A may use the logistics system to plan thelogistics. For example, the logistics system may detect availability oftransporter and assign suitable transporter based on transport sensors(e.g., truck ELD sensors 406, traffic sensors 407, GPS data from driverapp 411). In another example, the logistics system may also detectavailability of the payloads at various vendors based on real-timesensors (e.g., sand mine sensors 405 a, transload sensors 405 b, etc.).

Continuing with FIG. 25, at step 2506, the logistics system may relaythe order 2502 of payloads to Vendor B (e.g., payload vendor) who maysell/rent the payloads. In some embodiment, Vendor B may receive theorder 2502 after Vendor A has accepted the order and planned out thelogistics for the delivery of the payloads. In such case, Vendor B mayreceive the order 2502′s logistics information with appropriate orderinformation to reference it to customer data (e.g., the purchase order(PO) number, customer identifier, etc.). In some embodiments, bothVendor A and Vendor B may receive the order 2502 around the same periodof time. In some embodiments, Vendor B may receive the order 2502 beforeVendor A.

Continuing with FIG. 25, at step 2508, the logistics system may trackVendor A's data, for example, on logistics operations. The logisticssystem may track Vendor A's data on logistics operations, for example,by collecting various data from logistics sensors (e.g., payloadordering app 2702, logistics app 410, real-time sensors 401, driver app411, or any other sensors or apps). The logistics system, for example,can track Vendor A′s record of logistics operations such as the customeridentifier, the bill of lading (BOL) number, the purchase order (PO)number, the time the payloads were picked up by the transporter (e.g.,from barcodes read by scanner at the pick-up site, driver app 411,manual input, etc.); the payload quantity, type, and price (e.g., frombarcodes read by scanner at the pick-up site, weight sensors; driver app411, manual input, etc.); the current location of the payloads (e.g.,via GPS data from driver app 411, traffic sensors 407, etc.); variousinformation related to the transporter such as transporter identifier,for example, a name or number identifying a transporter such as atruck/train and/or a transporter company (e.g., data from driver app411); the time of payloads' drop-off to customer (e.g., from barcodesread by scanner at the drop-off site, driver app 411, etc.); and/or anyother record/data related to Vendor A's logistics operations, forexample, based on manual inputs from anyone involved with the logisticsoperations, any inputs such as document scanned using text recognition,and/or any other data entry methods. In some embodiments, the logisticssystem may track detailed timeline of Vendor A's logistics operation.For example, i) time the order 2502 was placed; ii) time the transporterbegan working; iii) time the transporter arrived at the pickup location;iv) time the transporter departed the pickup location; v) time thetransporter arrived at the drop-off location; vi) time the transporterdeparted the drop-off location; and vii) any other timeline on thelogistics operations associated with the order 2502.

In some embodiments, the logistics system may collect, compile, orupdate Vendor A's data (e.g., on logistics operations) in apredetermined configuration, for example, in a certain data format,table format, text format, sheet (e.g., spread sheet) format, time/dateformat, file name format, sheet name format, table name format, emailaddress format, or any other data structure/name formats.

Continuing with FIG. 25, at step 2510, the logistics system may track orreceive regular updates from Vendor B (e.g., payload vendor) on data,for example, on financial, billing, and transporter of the order 2502.The data related to the order 2502 from Vendor B, for example, can beupdated via email, via application programming interface (API)integration, or via any other electronic transmission. In someembodiments, the data related to the order 2502 from Vendor B caninclude a similar category of data from Vendor A (e.g., logisticsvendor).

In some embodiments, Vendor B can have an independent system forcollecting the financial/billing data and any other data related to theorder 2502. For example, Vendor B as the payload vendor, canindependently track the customer identifier; the bill of lading (BOL)number; the purchase order (PO) number; the payload quantity, type, andprice; a detailed timeline of the work performed on the payloads (e.g.,time the order 2502 was placed, time the transporter began working, timethe transporter arrived at the pickup location, time the transporterdeparted the pickup location, time the transporter arrived at thedrop-off location, time the transporter departed the drop-off location,and any other timeline of work performed on the payloads); transporteridentifier (e.g., a name or number identifying a transporter such as atruck and/or a transporter company); and/or any other record/datarelated to Vendor B′s operations, for example, based on manual inputsfrom anyone involved with the operations, any inputs such as documentscanned using text recognition, and/or any other data/informationgathering methods.

In some embodiments, Vendor B (e.g., payload vendor) may collect thedata using one or more sensors (e.g., time sensors, weight sensors,imaging sensors, bar code/card readers, RF ID sensors, non-contact radarsensors, acoustics-based “3D” sensors, mechanical “bob” sensors, guidedwave radar sensors, and/or laser level sensors), manual inputs, anyinputs such as document scanned using text recognition, and/or any otherdata/information gathering methods

In some embodiments, Vendor B's data (e.g., on payloads) may becollected, compiled, or updated in a predetermined configuration, whichcan be identical, similar, parallel, or analogous to the predeterminedconfiguration for collecting, compiling, or updating Vendor A's data(e.g., on logistics operations). In some embodiments, Vendor B mayregularly update the logistics system with the collected data. Forexample, Vendor B's independent system for collecting and compiling thedata may automatically send the gathered data to the logistics system ina predetermined configuration as described.

Continuing with FIG. 25, at step 2512, the logistics system canautomatically reconcile Vendor A's data (e.g., logistic vendor's data onlogistics operations) and Vendor B's data (e.g., payload vendor's dataon payloads). In some embodiments, Vendor A's data and Vendor B's datamay have been tracked and updated separately, and these two sets of datamay need to be reconciled as the order 2502 is being fulfilled or oncethe order 2502 has been fulfilled. In some embodiments, the logisticssystem can automatically match electronically tracked/updated data fromVendor B (e.g., payload vendor) with the Vendor A's data (e.g., onlogistics operations) that may have been already collected via variouslogistics sensors.

In some embodiments, the logistics system can detect the two sets ofdata (e.g., on the order 2502) from Vendor A and Vendor B for automaticreconciliation. For example, the logistics system can detect anycombination of the customer name, Vendor A's name, Vendor B's name, orany predetermined configuration (e.g., certain data format, tableformat, text format, sheet format, time/date format, file name format,sheet name format, table name format, email address format, or any otherdata structure/name formats) from Vendor A and B's data. For example, ifthe logistics system detects one set of data from Vendor A and anotherset of data from Vendor B with the same or similar customer name, thenthe logistics system may further match/compare the two data sets forautomatic reconciliation. In another example, the logistics system maydetect matching Vendor A or B's name from both data sets from Vendor Aand B, then the logistics system may further match/compare the data setsfor automatic reconciliation. In yet another example, if the data setson the order 2502 has been updated to the logistics system via email,the logistics system may detect certain preconfigured email addressformat to determine the data sets for further automatic reconciliation.In yet another example, the logistics system may detect certainpreconfigured file name, sheet name, table name, or any other data orname format to determine the data sets for further automaticreconciliation.

In some embodiments, once the logistics system determines the two setsof data from Vendor A and B that are to be matched, the logistics systemcan further automatically reconcile the data values within the two datasets. In some embodiments, the logistics system can automaticallyreconcile the two data sets based on optimized order of matching,reliability factor, high-value factor, and/or fuzzy logic algorithm.

In some embodiments, the logistics system can match data values in acertain order or give more weight to certain data values, for example,based on the reliability of the data collection process. For example, insome embodiments, data values collected automatically by reliablemachine-type (e.g., non-human) sensors can be more reliable than certaindata entries collected manually by a person. In some embodiments, datavalues collected by reliable machine-type sensors may be matched withhigher priority. In some embodiment, certain type of data may be ofhigher-value than other type of data. For example, depending on thecustomer or vendors, the quantity of the payload (e.g., the weight ofthe payload which can be directly associated with the cost of thepayload) can be more important than the pick-up time of the payload. Insuch cases, the logistics system can prioritize matching the high-valuedata. The high-value data, for example, can be the quantity of thepayload, price of the payload, the bill of lading (BOL) number, and/orany data related to the order 2502 as described herein, depending on thepreference(s) of the users.

In some embodiments, some data values within the data sets from Vendor Aand Vendor B may not be identical. For example, Vendor A's data (e.g.,on logistics operations) may indicate the payload pick-up time as 9:00am (e.g. collected via driver app 411, logistics app 410, etc.), whereasVendor B's data (e.g., on payloads) may have indicate the payloadpick-up time as 9:02 am (e.g., collected via Vendor B's independent timesensor(s) located at Vendor B's loading bay). In another example,certain data values within the data sets may be missing. In someembodiments, the logistics system can use a fuzzy logic algorithm tomatch the data values within the data sets that are not identical. Forexample, the logistics system can be configured with a fuzzy logicthreshold to determine what constitutes a match. For example, thelogistics system may determine that Vendor A's pick-up time data of 9:00am and Vendor B's pick-up time data of 9:02 am are a match, if the twovalues fall within a certain fuzzy logic threshold. In some embodiments,the logistics system's fuzzy logic algorithm can be optimized fordifferent categories of data and for different vendors. For example, ifa vendor's time sensor inaccurately measures time 5 minutes late, thenthe logistics system's fuzzy logic algorithm can be optimized toconsider such inaccuracy.

In some embodiments, at step 2516, the logistics system canautomatically detect the set of matched data and approve thebills/invoices from Vendors A and B that are related to the matched setof data. In some embodiment, the logistics system can also generate andmaintain a reconciled data base. For example, this reconciled data basemay include all of the high-value data (e.g., the payload quantity, thepayload price, the BOL number, etc.) that has been matched or mayinclude any other matched data associated with the order 2502. In someembodiments, the logistics system can generate and maintain thereconciled data base not only from the order 2502, but also from anyother orders that have been managed and automatically reconciled by thelogistics system. For example, as the logistics system manages andautomatically reconciles more and more orders, the logistics system maygenerate and maintain the reconciled data base associated with all ofthose orders. In some embodiments, at step 2514, the logistics systemcan automatically detect the set of unmatched data and flag thebills/invoices from Vendors A and B that are related to the unmatchedset of data for review by human (e.g., users). In some embodiments, thelogistics system may generate a report of unmatched set of data that canbe analyzed and used to further optimize the automatic reconciliationalgorithm. In some embodiments, the unmatched set of data that has beenreconciled based on human review may be updated to the reconciled database.

FIG. 26 shows a conceptual representation of an automatic reconciliationaccording to an exemplary embodiment. The automatic reconciliation 2600may automatically reconcile various data (e.g., sensor data, customerdata, financial data, billing data, shipment data, transporter data,etc.) with each other and may reduce the need for human approval beforethe invoices can be submitted for invoicing or payment. In someembodiments, the automatic reconciliation 2600 may involve data fromthree exemplary parties: mutual customer 2602, logistics vendor 2604,and payload vendor 2606. For example, when the mutual customer 2602submits an order for payloads, the logistics vendor 2604 may provide thedelivery service for the mutual customer 2602, and the payload vendor2606 may provide (e.g. sell or rent) the payloads for the mutualcustomer 2602. Various data associated with logistics vendor and variousdata associated with payload vendor can be automatically reconciled, forexample, using the automatic reconciliation method 2500 illustrated inFIG. 25. This automatic reconciliation can help reconcile the payloaddata and/or invoices from the logistics and payload vendors and reduce90% or higher manual approvals (e.g., almost everything can beautomatically verified and reconciled).

FIG. 27 illustrates a logistics system with an exemplary payloadordering module according to an exemplary embodiment. A logistics system2700 can be similar to the logistics system 400 in FIG. 4. The logisticssystem 2700 may, for example, include a payload ordering app 2702component. The payload ordering app 2702, for example, can be used bycustomers to order various payloads for fracking operations. In someembodiments, the payload ordering app 2702 and the logistics app 410 maybe embodied together in one or more apps, which may operate on one ormore devices. Payload ordering app 2702 is illustrated as an app forillustrative purposes, but in some exemplary embodiments, any suitablehardware and/or software may be used, such desktop computers, laptopcomputers, specialized computing devices, and other hardware and/orsoftware applications.

Various modifications and additions can be made to the exemplaryembodiments discussed without departing from the scope of the presentinvention. For example, while the embodiments described above refer toparticular features, the scope of this invention also includesembodiments having different combinations of features and embodimentsthat do not include all of the described features. Accordingly, thescope of the present invention is intended to embrace all suchalternatives, modifications, and variations as fall within the scope ofthe claims, together with all equivalents thereof.

What is claimed is:
 1. A system for resource transportation, the systemcomprising: a routing command subsystem configured to be communicablycoupled to: a first input device at a first location, the first inputdevice configured to determine a first resource factor of a resource atthe first location; a location input device associated with atransporter, the transporter configured to transport the resource, thelocation input device configured to determine a transporter location,the routing command subsystem further configured to change a firstendpoint of a transporter route to a first alternate location based atleast in part on the first resource factor and the transporter location.2. The system for resource transportation of claim 1, wherein the firstlocation comprises a supply node.
 3. The system for resourcetransportation of claim 1, wherein the first location comprises a demandnode.
 4. The system for resource transportation of claim 1, the systemfurther comprising a second input device at a second location, thesecond input device configured to determine a second resource factor atthe second location; wherein the changing the first endpoint of thetransporter route further comprising changing the first endpoint of thetransporter route to the first alternate location based at least in parton the second resource factor.
 5. The system for resource transportationof claim 4, wherein the first location comprises a source node and thesecond location comprises a destination node.
 6. The system for resourcetransportation of claim 1, wherein the first input device comprises asensor.
 7. The system for resource transportation of claim 1, whereinthe first input device comprises a data entry device.
 8. The system forresource transportation of claim 1, wherein the location input devicecomprises a sensor.
 9. The system for resource transportation of claim1, wherein the location input device comprises a data entry device. 10.The system for resource transportation of claim 1, wherein the firstresource factor comprises an amount of the resource.
 11. The system forresource transportation of claim 10, wherein the amount of the resourcecomprises a quantity of the resource at the first location.
 12. Thesystem for resource transportation of claim 10, wherein the amount ofthe resource comprises a quantity of the resource consumed at the firstlocation.
 13. The system for resource transportation of claim 1, whereinthe first resource factor comprises a rate of change of the resource.14. The system for resource transportation of claim 1, wherein the firstresource factor comprises a comparison of a quantity of the resourcewithdrawn at a supply node with a scheduled demand for the resource at ademand node.
 15. The system for resource transportation of claim 1,wherein the first input device determines the first resource factorbased at least in part on a real-time input.
 16. The system for resourcetransportation of claim 1, wherein the first input device determines thefirst resource factor based at least in part on a periodic input. 17.The system for resource transportation of claim 1, wherein the locationinput device determines the transporter location based at least in parton a real-time input.
 18. The system for resource transportation ofclaim 1, wherein the location input device determines the transporterlocation based at least in part on a periodic input.
 19. The system forresource transportation of claim 1, further comprising a second inputdevice associated with the transporter, the second input deviceconfigured to determine a second resource factor of the resourceassociated with the transporter.
 20. The system for resourcetransportation of claim 19, wherein the second resource factor comprisesan amount of the resource transported by the transporter.
 21. The systemfor resource transportation of claim 1, wherein the first endpoint ofthe transporter route comprises a supply node.
 22. The system forresource transportation of claim 1, wherein the first endpoint thetransporter route comprises a demand node.
 23. The system for resourcetransportation of claim 1, the routing command subsystem furtherconfigured to determine a transporter resource factor, wherein thetransporter resource factor comprises a number of transporterstransporting the resource.
 24. The system for resource transportation ofclaim 1, the routing command subsystem further configured to direct asecond transporter to transport the resource based at least in part onthe first resource factor and the transporter location.
 25. The systemfor resource transportation of claim 1, the routing command subsystemfurther configured to change a schedule for the transporter to transportthe resource based at least in part on the first resource factor and thetransporter location.
 26. The system for resource transportation ofclaim 1, the routing command subsystem further configured to change aroute of the transporter to include a first intermediate location basedat least in part on the first resource factor and the transporterlocation.
 27. The system for resource transportation of claim 1, whereinthe changing the first endpoint of the transporter route furthercomprising predicting an availability of the resource at a supply node.28. The system for resource transportation of claim 1, wherein thechanging the first endpoint of the transporter route further comprisingpredicting a demand of the resource at a demand node.
 29. The system forresource transportation of claim 1, wherein the changing the firstendpoint of the transporter route further comprising predicting a numberof transporters available to transport the resource.
 30. The system forresource transportation of claim 1, wherein the first resource factordepends on a second resource factor associated with a second resource.31. The system for resource transportation of claim 1, wherein theresource comprises at least one sand, chemicals, water, oil, gas,equipment, or personnel.
 32. The system for resource transportation ofclaim 1, wherein the transporter comprises at least one of a rail car, atruck, or pipeline.
 33. The system for resource transportation of claim1, wherein the first input device comprises at least one of a node sitesensor, a fracking van sensor, or a supply node sensor.
 34. The systemfor resource transportation of claim 1, wherein the routing commandsubsystem further configured to change the first endpoint of thetransporter route to the first alternate location based at least in parton the first resource factor, the transporter location, and a driverfactor.
 35. The system for resource transportation of claim 34, whereinthe driver factor comprises at least one of a current duty status or anumber of hours of service remaining.
 36. A system for resourcetransportation, the system comprising: a transporter selection subsystemconfigured to be communicably coupled to: a first input device at afirst location, the first input device configured to determine a firstresource factor of a resource at the first location; a first locationinput device associated with a first transporter, the first locationinput device configured to determine a first transporter location; asecond location input device associated with a second transporter, thefirst location input device configured to determine a second transporterlocation; a driver input device associated with a driver, the driverinput device configured to determine a driver factor; and thetransporter selection subsystem further configured to select at leastone of the first transporter or the second transporter to transport theresource based at least in part on the first resource factor, the firsttransporter location, the second transporter location, and the driverfactor.
 37. The system for resource transportation of claim 36, whereinthe first location comprises a supply node.
 38. The system for resourcetransportation of claim 36, wherein the first location comprises ademand node.
 39. The system for resource transportation of claim 36,wherein the first input device comprises a sensor.
 40. The system forresource transportation of claim 36, wherein the first input devicecomprises a data entry device.
 41. The system for resourcetransportation of claim 36, wherein the first location input devicecomprises a sensor.
 42. The system for resource transportation of claim36, wherein the first location input device comprises a data entrydevice.
 43. The system for resource transportation of claim 36, whereinthe second location input device comprises a sensor.
 44. The system forresource transportation of claim 36, wherein the second location inputdevice comprises a data entry device.
 45. The system for resourcetransportation of claim 36, wherein the first resource factor comprisesan amount of the resource.
 46. The system for resource transportation ofclaim 45, wherein the amount of the resource comprises a quantity of theresource at the first location.
 47. The system for resourcetransportation of claim 45, wherein the amount of the resource comprisesa quantity of the resource consumed at the first location.
 48. Thesystem for resource transportation of claim 36, wherein the firstresource factor comprises a rate of change of the resource.
 49. Thesystem for resource transportation of claim 36, wherein the firstresource factor comprises a comparison of a quantity of the resourcewithdrawn at a supply node with a scheduled demand for the resource at ademand node.
 50. The system for resource transportation of claim 36,wherein the first input device determines the first resource factorbased at least in part on a real-time input.
 51. The system for resourcetransportation of claim 36, wherein the first input device determinesthe first resource factor based at least in part on a periodic input.52. The system for resource transportation of claim 36, wherein thefirst location input device determines the transporter location based atleast in part on a real-time input.
 53. The system for resourcetransportation of claim 36, wherein the first location input devicedetermines the transporter location based at least in part on a periodicinput.
 54. The system for resource transportation of claim 36, whereinthe second location input device determines the transporter locationbased at least in part on a real-time input.
 55. The system for resourcetransportation of claim 36, wherein the second location input devicedetermines the transporter location based at least in part on a periodicinput.
 56. The system for resource transportation of claim 36, furthercomprising a second input device associated with the transporter, thesecond input device configured to determine a second resource factor ofthe resource associated with the transporter.
 57. The system forresource transportation of claim 56, wherein the second resource factorcomprises an amount of the resource transported by the transporter. 58.The system for resource transportation of claim 36, the transporterselection subsystem further configured to determine a transporterresource factor, wherein the transporter resource factor comprises anumber of transporters transporting the resource.
 59. The system forresource transportation of claim 36, the transporter selection subsystemfurther configured to direct a second transporter to transport theresource based at least in part on the first resource factor and thefirst transporter location.
 60. The system for resource transportationof claim 36, the transporter selection subsystem further configured tochange a schedule for the transporter to transport the resource based atleast in part on the first resource factor and the first transporterlocation.
 61. The system for resource transportation of claim 36, therouting command subsystem further configured to change a route of thetransporter to include a first intermediate location based at least inpart on the first resource factor and the first transporter location.62. The system for resource transportation of claim 36, wherein thefirst resource factor depends on a second resource factor associatedwith a second resource.
 63. The system for resource transportation ofclaim 36, wherein the driver factor comprises at least one of a currentduty status or a number of hours of service remaining.
 64. A system forresource transportation, the system comprising: a routing commandsubsystem configured to be communicably coupled to: an amount sensor ata production operation at a first location, the amount sensor configuredto determine an amount of a resource at the production operation, theproduction operation using the resource at a use rate that varies overtime, wherein the use rate further depends on availability of otherresources at the first location; a location sensor associated with atransporter, the transporter configured to transport the resource, thelocation sensor configured to determine a transporter location, therouting command subsystem further configured to: receive informationabout the use rate; and reroute the transporter based at least in parton the amount and the transporter location.
 65. The system of claim 64,wherein the resource comprises sand.
 66. The system of claim 64, whereinthe transporter comprises a rail car.
 67. The system of claim 64,wherein the transporter comprises a truck.
 68. The system of claim 64,wherein the amount sensor comprises at least one of a site sensor, afracking van sensor, a sand mine sensor, or a transload sensor.
 69. Thesystem of claim 64, wherein rerouting the transporter comprises routingthe transporter via a transload.
 70. The system of claim 64, wherein therouting command subsystem is further configured to reroute thetransporter based in part on a quantity of the resource at the firstlocation.
 71. The system of claim 64, wherein the routing commandsubsystem is further configured to reroute the transporter based in parton a total quantity of the resource consumed at the first location. 72.The system of claim 64, wherein the routing command subsystem is furtherconfigured to reroute the transporter based in part on a predictedconsumption rate at the first location.
 73. The system of claim 64,wherein the routing command subsystem is further configured to reroutethe transporter based in part on a predicted consumption rate at asecond site.